GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with early- (...) to middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (shrink)

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based (...) splitting method. Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error. As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%. (shrink)

Systemic lupus erythematosus is an inflammatory autoimmune disease with depression as one of its most common symptoms. The aim of this study is to establish a nomogram prediction model to assess the occurrence of depression in patients with SLE. Based on the Hospital Anxiety and Depression Scale cutoff of 8, 341 patients with SLE, recruited between June 2017 and December 2019, were divided into depressive and non-depressive groups. Data on socio-demographic characteristics, medical history, sociopsychological factors, and other (...) risk factors were collected. Between-group differences in clinical characteristics were assessed with depression as the dependent variable and the variables selected by logistic multiple regression as predictors. The model was established using R language. Marital status, education, social support, coping, and anxiety predicted depression. The nomogram prediction model showed that the risk rate was from 0.01 to 0.80, and the receiver operating characteristic curve analysis showed that the area under the curve was 0.891. The calibration curve can intuitively show that the probability of depression predicted by the nomogram model is consistent with the actual comparison. The designed nomogram provides a highly predictive assessment of depression in patients with SLE, facilitating more comprehensive depression evaluation in usual clinical care. (shrink)

PurposeThe purpose of this study was to introduce an orthogonal experimental design to improve the efficiency of building and optimizing models for freezing of gait prediction.MethodsA random forest model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG. An OED was introduced to optimize the feature extraction parameters.ResultsThe main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction (...) time were 27%, 68%, and 2.99 s, respectively.ConclusionThe OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model. (shrink)

Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis and principal component analysis, are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The (...) different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance. (shrink)

Though predictive processing approaches to the mind were originally applied to exteroceptive perception, i.e., vision and action, recent work has started to explore the role of interoceptive perception, i.e., emotion and affect. This article builds on this work by extending PP beyond emotion to the construction of emotional dispositions. I employ principles from dynamical systems theory and PP to provide a model of how dispositional anger can develop in response to early experiences of psychosocial stress. The model is (...) then deployed to explain the established link between psychosocial stress in early life and the appearance of certain organic disease phenotypes in later life. This phenomenon can appear mysterious when viewed through the standard biomedical explanatory lens, which has difficulty accounting for the causal influence of subjective perceptions and evaluations of the social and material environment on the development of organic disease processes. The model provided presents such cases as instances of developmental mismatch. They occur when an organism develops an emotional disposition that leads them to make habitually-biased appraisals of what the social environment affords. The model provides a novel explanation of certain organic disease phenotypes with top-down and developmental causes, and demystifies one class of cases involving apparently spooky ‘mind-to-matter’ causation. (shrink)

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that (...) topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the (...) majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. BioEssays 30:707–710, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network model to predict the occurrences of malaria disease. The proposed BN (...) class='Hi'>model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table also becomes more complex. (shrink)

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical (...) question about how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding. (shrink)

ArgumentThis article uses four historical case studies to address epistemological issues related to the animal model of human diseases and its use in medical research on human diseases. The knowledge derived from animal models is widely assumed to be highly valid and predictive of reactions by human organisms. In this contribution, I use three significant historical cases of failure (ca. 1890, 1960, 2006), and a closer look at the emergence of the concept around 1860/70, to elucidate core assumptions related (...) to the specific practices of animal-human knowledge transfer, and to analyze the explanations provided by historical actors after each of the failures. Based on these examples, I argue that the epistemological status of the animal model changed from that of a helpful methodological tool for addressing specific questions, but with precarious validity, to an obligatory method for the production of strong knowledge on human diseases. As a result, there now exists a culture of biomedical research in human disease that, for more than a century, has taken the value of this methodological tool as self-evident, and more or less beyond question. (shrink)

BackgroundMore than 15% of Chinese respondents reported somatic symptoms in the last week of January 2020. Promoting resilience is a possible target in crisis intervention that can alleviate somatization.ObjectivesThis study aims to investigate the relationship between resilience and somatization, as well as the underlying possible mediating and moderating mechanism, in a large sample of Chinese participants receiving a crisis intervention during the coronavirus disease 2019 epidemic.MethodsParticipants were invited online to complete demographic information and questionnaires. The Symptom Checklist-90 somatization subscale, (...) 10-item Connor–Davidson resilience scale, and 10-item Perceived Stress Scale were measured.ResultsA total of 2,557 participants were included. Spearman correlation analysis revealed that lower resilience was associated with more somatic symptoms. The conditional process model was proved. The interaction effects between perceived stress and sex predicted somatization.ConclusionResilience is a key predictor of somatization. The mediating effects of perceived stress between resilience and somatization work in the context of sex difference. Sex-specific intervention by enhancing resilience is of implication for alleviating somatization during the coronavirus disease 2019 epidemic. (shrink)

Thresholds for disease extinction provide essential information for the prevention and control of diseases. In this paper, a stochastic epidemic model, a continuous-time Markov chain, for the transmission dynamics of West Nile virus in birds is developed based on the assumptions of its analogous deterministic model. The branching process is applied to derive the extinction threshold for the stochastic model and conditions for disease extinction or persistence. The probability of disease extinction computed from the (...) branching process is shown to be in good agreement with the probability approximated from numerical simulations. The disease dynamics of both models are compared to ascertain the effect of demographic stochasticity on West Nile virus dynamics. Analytical and numerical results show differences in model predictions and asymptotic dynamics between stochastic and deterministic models that are crucial for the prevention of disease outbreaks. It is found that there is a high probability of disease extinction if the disease emerges from exposed mosquitoes unlike if it emerges from infectious mosquitoes and birds. Finite-time to disease extinction is estimated using sample paths and it is shown that the epidemic duration is shortest if the disease is introduced by exposed mosquitoes. (shrink)

Cardiovascular disease is a major complication of type 2 diabetes mellitus. In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, (...) the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve analysis was used to evaluate the model performance. Of participants, 272 were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion. (shrink)

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system, and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a (...) disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes dataset using accuracy and mean squared error as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. (shrink)

Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases. In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle (...) consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks (...) to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually (...) used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic curve. In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource. (shrink)

The paper presents the second part of the study of “sponsored goods” in the cultural sector. It describes economic activities of theaters, concert organizations and museums in three dimensional index space coordinate axes being the lag of labor productivity, faster growth of salaries and tickets prices in relation to the corresponding macroeconomic indices. The methodology of constructing such indexes and statistical data used for this purpose are described in the first article published under the title “Symptoms and consequences of Baumol’s (...) cost disease in cultural sector: an econometric study of “sponsored goods”. The aim of the present article is to construct and analyse econometric model of cost disease based on statistical data on activities of Russian theaters, concert organizations and museums for the period of 1994-2010. The regression analysis based on structural model that reflects theoretically predictable links was chosen as a method of study. Such links include first, influence on cost growth of lag of labour productivity in arts organisations and parallel trend in salaries. Second, dependence of increase of income of such organizations on dynamics of labour productivity and ticket prices, third, balanced equation - product of equilibrium conditions for sponsored goods that describes the interrelation between costs of production, prices and budget subsidies. The calculations made it possible to construct basic and modified econometric models that establish statistical correlations between income deficit and three regressors, namely indexes of labour productivity, salaries and ticket prices. As a result of this model the article presents projections of regression relation of income deficit on «expenditures plane», «income plane» and «economic results plane». Theequations derived allow to conclude that there is a statistically significant income deficit negative elasticity by labour productivity index, positive elasticity by salaries index and ticket price index, as well as rising dynamics of Russian performing arts organization income deficit and its compensation in the nearest future. (shrink)

The susceptible-transmissible-removed (STR) model is a deterministic compartment model, based on the susceptible-infected-removed (SIR) prototype. The STR replaces 2 SIR assumptions. SIR assumes that the emigration rate (due to death or recovery) is directly proportional to the infected compartment’s size. The STR replaces this assumption with the biologically appropriate assumption that the emigration rate is the same as the immigration rate one infected period ago. This results in a unique delay differential equation epidemic model with the delay (...) equal to the infected period. Hamer’s mass action law for epidemiology is modified to resemble its chemistry precursor—the law of mass action. Constructing the model for an isolated population that exists on a surface bounded by the extent of the population’s movements permits compartment density to replace compartment size. The STR reduces to a SIR model in a timescale that negates the delay—the transmissible timescale. This establishes that the SIR model applies to an isolated population in the disease’s transmissible timescale. Cyclical social interactions will define a rhythmic timescale. It is demonstrated that the geometric mean maps transmissible timescale properties to their rhythmic timescale equivalents. This mapping defines the hybrid incidence (HI). The model validation demonstrates that the HI-STR can be constructed directly from the disease’s transmission dynamics. The basic reproduction number (R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0$$\end{document}) is an epidemic impact property. The HI-STR model predicts that R0∝ρnB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0 \propto \root \mathfrak{B} \of {\rho_n}$$\end{document} where ρn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho_n$$\end{document} is the population density, and B\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathfrak{B}}$$\end{document} is the ratio of time increments in the transmissible- and rhythmic timescales. The model is validated by experimentally verifying the relationship. R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0$$\end{document}’s dependence on ρn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho_n$$\end{document} is demonstrated for droplet-spread SARS in Asian cities, aerosol-spread measles in Europe and non-airborne Ebola in Africa. (shrink)

Explanation and prediction play an important role in medical decision making, particularly for diagnostic and treatment decisions. For the most part, explanations as well as predictions are derived from causal knowledge and have to be made under uncertainty. In cognitive psychology, these phenomena have been approached from two directions. On the one hand, there is research on knowledge representation and inferential reasoning (Holland et al. 1986; Anderson 1990). On the other hand, there is research on heuristics and biases in (...) judgments under uncertainty (Kahneman et al. 1982; Rehm & Gadenne 1990). However, no exact models have yet been developed to describe the process that leads from a given knowledge base to judgments of probabilities, i.e., from causal knowledge about the world to subjective expressions of the uncertainty associated with explanations or predictions. (shrink)

Rid and Wendler propose the development of a Patient Preference Predictor (PPP), an actuarial model for predicting incapacitated patient’s life-sustaining treatment preferences across a wide range of end-of-life scenarios. An actuarial approach to end-of-life decision making has enormous potential, but transferring the logic of actuarial prediction to end-of-life decision making raises several conceptual complexities and logistical problems that need further consideration. Actuarial models have proven effective in targeted prediction tasks, but no evidence supports their effectiveness in the (...) kind of broad spectrum prediction task that is the proposed goal of the PPP. We argue that a more focused approach, targeting specific medical conditions and generating treatment predictions based on the preferences of individuals with actual disease experience, is both more firmly grounded in past research and is a more prudent initial strategy for exploring the efficacy of actuarial prediction in end-of-life decision making. (shrink)

The past few years have brought significant breakthroughs in understanding human genetics. This knowledge has been used to develop ‘polygenic scores’ which provide probabilistic information about the development of polygenic conditions such as diabetes or schizophrenia. They are already being used in reproduction to select for embryos at lower risk of developing disease. Currently, the use of polygenic scores for embryo selection is subject to existing regulations concerning embryo testing and selection. Existing regulatory approaches include ‘disease-based' models which (...) limit embryo selection to avoiding disease characteristics, and 'laissez-faire' or 'libertarian' models, under which embryo testing and selection remain unregulated. We introduce a novel 'Welfarist Model' which limits embryo selection according to the impact of the predicted trait on well-being. We compare the strengths and weaknesses of each model as a way of regulating polygenic scores. Polygenic scores create the potential for existing embryo selection technologies to be used to select for a wider range of predicted genetically influenced characteristics including continuous traits. Indeed, polygenic scores exist to predict future intelligence, and there have been suggestions that they will be used to make predictions within the normal range in the USA in embryo selection. We examine how these three models would apply to the prediction of non-disease traits such as intelligence. The genetics of intelligence remains controversial both scientifically and ethically. This paper does not attempt to resolve these issues. However, as with many biomedical advances, an effective regulatory regime must be in place as soon as the technology is available. If there is no regulation in place, then the market effectively decides ethical issues. (shrink)

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we (...) make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked. (shrink)

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. (...) These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission—aiding infection control measures and policies—emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: automation and algorithmic reliance impacting freedom of choice, big data analytics complexity impacting informed consent, reliance on profiling impacting individual and group identities and justice/fair access and increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement. (shrink)

While explaining a large proportion of any variance, accounts of the speed and accuracy of targetting movements use techniques (e.g., log transforms) that typically reduce variability before ''explaining'' the data. Therefore the predictive power of such accounts are important. We consider whether Plamondon's model can account for kinematics of targetting movements of clinical populations.

Alzheimer's Disease is a form of dementia that affects the memory, cognition, and motor skills of patients. Extensive research has been done to develop accessible, cost-effective, and non-invasive techniques for the automatic detection of AD. Previous research has shown that speech can be used to distinguish between healthy patients and afflicted patients. In this paper, the ADReSS dataset, a dataset balanced by gender and age, was used to automatically classify AD from spontaneous speech. The performance of five classifiers, as (...) well as a convolutional neural network and long short-term memory network, was compared when trained on audio features and text features. The same audio and text features were used to train five regression models to predict the Mini-Mental State Examination score for each patient, a score that has a maximum value of 30. The top-performing classification models were the support vector machine and random forest classifiers trained on BERT embeddings, which both achieved an accuracy of 85.4% on the test set. The best-performing regression model was the gradient boosting regression model trained on BERT embeddings and CLAN features, which had a root mean squared error of 4.56 on the test set. The performance on both tasks illustrates the feasibility of using speech to classify AD and predict neuropsychological scores. (shrink)

Most contemporary models of disease development consider the interaction between genotype and environment as static. The authors argue that because time is a key factor in genotype–environment interaction, this approach oversimplifies the pathology analysis and may lead to wrong conclusions. In reviewing the field, the authors suggest that the history of genotype–environment interactions plays an important role in the development of diseases and that this history may be analyzed using the phenotype as a proxy. Furthermore, a theoretical and experimental (...) framework is proposed based on the assumption that phenotypes do not change from one to another randomly but are interconnected and follow certain phenotype trajectories. It then follows that analysis of such phenotype trajectories might be useful to predict the future phenotypes including the onset of disease. In addition, an analysis of phenotype trajectories can be subsequently used to choose better control subjects in comparative studies reducing noise and bias in studies investigating disease mechanisms. (shrink)

PurposeThe primary angle-closure glaucoma is an irreversible blinding eye disease in the world. Previous neuroimaging studies demonstrated that PACG patients were associated with cerebral changes. However, the effect of optic atrophy on local and remote brain functional connectivity in PACG patients remains unknown.Materials and MethodsIn total, 23 patients with PACG and 23 well-matched Health Controls were enrolled in our study and underwent resting-state functional magnetic resonance imaging scanning. The regional homogeneity method and functional connectivity method were used to evaluate (...) the local and remote brain functional connectivity. Moreover, support vector machine method was applied to constructing PACG classification model.ResultsCompared with the HC, PACG patients showed increased ReHo values in right cerebellum _8, left CER_4-5, and right CER_8. In contrast, PACG patients showed decreased ReHo values in the bilateral lingual gyrus /calcarine /superior occipital gyrus and right postcentral gyrus. The ReHo value exhibited an accuracy of 91.30% and area under curve of 0.95 for distinguishing the PACG patients from HC.ConclusionOur study demonstrated that the PACG patients showed abnormal ReHo value in the cerebellum, visual cortex, and supplementary motor area, which might be reflect the neurological mechanisms underlying vision loss and eye pain in PACG patients. Moreover, the ReHo values can be used as a useful biomarker for distinguishing the PACG patients from HCs. (shrink)

Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using (...) degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development. (shrink)

An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, (...) what is more properly called ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios. (shrink)

According to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza subtypes against which there is little or no preexisting human immunity. Such subtypes of influenza have the potential to cause devastating epidemics. Thus, enhancing surveillance systems for the purpose of detecting influenza epidemics in an early stage can (...) quicken response times and save millions of lives. This paper presents three adapting intelligence models: support vector machine regression, artificial neural network using particle swarm optimisation, and our intelligent time series to predict influenza epidemics. The novelty of the current study is that it proposes a new intelligent model to predict influenza outbreaks. The INTS model combines clustering with a time series model to enhance the prediction of influenza outbreaks. The innovation of our proposed model integrates the results obtained from the existing weighted exponential smoothing model with centroids obtained from clustering. We developed a surveillance system for influenza epidemics using Google search queries. The current research is based on a weighted version of the Center for Disease Control and Prevention influenza-like illness activity level obtained from the Center for Disease Control and Prevention data, as well as query data obtained from the Goggle search engine in the USA. The influenza-like illness data was collected from January 4, 2009, to December 27, 2015, stretching across a total time span of 312 weeks. Google Correlate was used to select search queries related to influenza epidemics. In total, 100 search queries were obtained from Google Correlate, 10 of which were better and more relevant search queries selected in this study. The model was evaluated using online Google search queries collected from Google Correlate. Standard measure performance MSE, RMSE, and MAE were employed to estimate the results of the proposed model. The empirical results of the INTS model showed MSE = 0.003, RMSE = 0.036, and MAE = 0.0185, indicating that the errors of the proposed model are very limited. A comparative model of predicting results between the INTS model, alternative Google Flu Trend, and autoregression with Google search data is also presented. The proposed model outperformed the existing models. (shrink)

The lockdown resulting from coronavirus disease 2019 has had a huge impact on peoples’ health. In sport specifically, athletes have had to deal with frustration of their objectives and changes in their usual training routines. The challenging and disruptive situation could hold implications for their well-being. This study examined the effect of the COVID-19 lockdown on changes in athletes’ reported eudaimonic well-being and goal motives over time. The relationship of resilience to changes in subjective vitality was also determined, and (...) changes in athletes’ goal motives were examined as potential mediators. Participants were 127 Spanish university athletes aged between 18 and 34 years. Approximately 4 months before the start of the lockdown in Spain, athletes responded to a questionnaire assessing their resilience, goal motives, and subjective vitality. Around 6 months later into the lockdown period, athletes’ goal motives and subjective vitality were assessed again. Growth modeling using hierarchical linear models revealed a significant decrease of autonomous goal motives and subjective vitality during the lockdown, but athletes did not show change over time in controlled goal motives. Path analysis, adjusting T2 measures for their corresponding T1 measures, showed that resilience significantly predicted changes in athletes’ autonomous goal motives, which then accounted for changes in subjective vitality. The indirect effect was significant. Resilience did not predict changes in athletes’ controlled goal motives. However, changes in controlled goal motives negatively predicted changes in subjective vitality during lockdown. The findings suggest negative impacts of the COVID-19 lockdown on athletes’ goal motives and eudaimonic well-being. Results also support the hypothesized mediational role of autonomous goal motives in the relationship between resilience and subjective vitality during the lockdown. As such, findings confirm the relevance of resilience to a key feature of athletes’ eudaimonic well-being and the importance of enhancing their autonomous goal striving. (shrink)

Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement allows healthcare sectors to consolidate and analyze massive health-based data to discover risks by making more accurate predictions. Therefore, this work proposes a real-time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate risk (...) in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time-series dataset is extracted from Medical Information Mart for Intensive Care. Recurrent neural network, long short-term memory, gated recurrent units, and bidirectional long short-term memory are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance. (shrink)

Background and PurposeTranscranial direct current stimulation is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS by analyzing the ctDCS-induced functional network alterations.MethodsEither the active 5-day, −1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS (...) were compared employing graph theoretical analysis based on the functional magnetic resonance imaging data. A support vector machine prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers.ResultsOur results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS. Our prediction model achieved the mean prediction accuracy of 68.3% after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance.ConclusionThe study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome. (shrink)

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder, definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls. To enhance the interpretability of the machine learning (...) model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule and regional gray matter volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases. (shrink)

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. (...) Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618. (shrink)

Recurrent, unvarying, and seemingly purposeless patterns of action and cognition are part of normal development, but also feature prominently in several neuropsychiatric conditions. Repetitive stereotyped behaviors can be viewed as exaggerated forms of learned habits and frequently correlate with alterations in motor, limbic, and associative basal ganglia circuits. However, it is still unclear how altered basal ganglia feedback signals actually relate to the phenomenological variability of RSBs. Why do behaviorally overlapping phenomena sometimes require different treatment approaches−for example, sensory shielding strategies (...) versus exposure therapy for autism and obsessive-compulsive disorder, respectively? Certain clues may be found in recent models of basal ganglia function that extend well beyond action selection and motivational control, and have implications for sensorimotor integration, prediction, learning under uncertainty, as well as aesthetic learning. In this paper, we systematically compare three exemplary conditions with basal ganglia involvement, obsessive-compulsive disorder, Parkinson’s disease, and autism spectrum conditions, to gain a new understanding of RSBs. We integrate clinical observations and neuroanatomical and neurophysiological alterations with accounts employing the predictive processing framework. Based on this review, we suggest that basal ganglia feedback plays a central role in preconditioning cortical networks to anticipate self-generated, movement-related perception. In this way, basal ganglia feedback appears ideally situated to adjust the salience of sensory signals through precision weighting of new sensory information, relative to the precision of predictions based on prior generated models. Accordingly, behavioral policies may preferentially rely on new data versus existing knowledge, in a spectrum spanning between novelty and stability. RSBs may then represent compensatory or reactive responses, respectively, at the opposite ends of this spectrum. This view places an important role of aesthetic learning on basal ganglia feedback, may account for observed changes in creativity and aesthetic experience in basal ganglia disorders, is empirically testable, and may inform creative art therapies in conditions characterized by stereotyped behaviors. (shrink)

Humans can recollect past events in details and/or know that an object, person, or place has been encountered before. During the last two decades, there has been intense debate about how recollection and familiarity are organized in the brain. Here, we propose an integrative memory model which describes the distributed and interactive neurocognitive architecture of representations and operations underlying recollection and familiarity. In this architecture, the subjective experience of recollection and familiarity arises from the interaction between core systems and (...) an attribution system. By integrating principles from current theoretical views about memory functioning, we provide a testable framework to refine the prediction of deficient versus preserved mechanisms in memory-impaired populations. The case of Alzheimer's disease is considered as an example because it entails progressive lesions starting with limited damage to core systems before invading step-by-step most parts of the model-related network. We suggest a chronological scheme of cognitive impairments along the course of AD, where the inaugurating deficit would relate early neurodegeneration of the perirhinal/anterolateral entorhinal cortex to impaired familiarity for items that need to be discriminated as viewpoint-invariant conjunctive entities. The integrative memory model can guide future neuropsychological and neuroimaging studies aiming to understand how such a network allows humans to remember past events, to project into the future, and possibly also to share experiences. (shrink)

Inflammatory bowel disease is chronic and incurable. Imperious diarrhea, rectal bleeding, fatigue, and weight loss, the main manifestations, cause a decrease in the quality of the patient’s personal and professional life. The objectives of this study were to identify a possible relationship between early maladaptive schemas and disease activity status using logistic regression, to identify the prevalence of early maladaptive schemes in patients and to propose a psychotherapeutic intervention plan. The following were found in a sample of 46 (...) patients aged 16–76 years. An increase in the domain overvigilance and inhibition score had a significant effect, with an increase of 1.137 CI95% [1.031, 1.254] of the risk of the disease being diagnosed as active. High and very high scores were observed for the emotional deprivation scheme and dependence/incompetence, vulnerability to harm and illness and subjugation schemas. The results show that the proposed model could predict and reconfirm the diagnosis; patients have specific psychotherapeutic needs. The therapeutic goal would be to offer care, empathy and protection, to strengthen self-confidence, to make patients realize that they have the ability to cope, to provide permission, encourage the patient to experiment, and guide the patient to express their anger healthily. The therapy scheme’s intervention could lead to increased long-term disease management capacity and, consequently, reduce costs directly and indirectly caused by this condition. (shrink)

The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and (...) scale—if and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self‐conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine. (shrink)

Since the outbreak of coronavirus disease 2019 in China, people have been exposed to a flood of media news related to the pandemic every day. Studies have shown that media news about public crisis events have a significant impact on individuals' depression. However, how and when the duration of attention to pandemic news predicts depression still remains an open question. This study established a moderated mediating model to investigate the relationship between the duration of attention to pandemic news (...) and depression, the mediating effect of risk perception, and the moderating effect of future time perspective on the relationship. In early February 2020, 701 individuals from 29 provinces, autonomous regions, and municipalities across China were asked to self-report their duration of attention to pandemic news, level of depression, risk perception, and future time perspective during the COVID-19 outbreak. Results show that there is a significant positive correlation between the duration of attention to news on COVID-19 and depression; risk perception mediates the association between the duration of attention to pandemic news and depression; and future time perspective plays a moderating role between risk perception and depression. The findings of the present study provide theoretical implications and practically throw some light on alleviating the public's depression during pandemic periods. We highlight that the individual's hope for a better future, focusing on positive news, and time perspective balance during an epidemic disease are also beneficial to promoting positive emotion and reducing depression. (shrink)

The challenges posed by chronic illness have pointed out to epidemiologists the multifactorial complex nature of disease causality. This notion has been referred to as a web of causality. This web extends theoretically beyond risk markers. It includes determinants of emergence/non-emergence of disease. This web of determinants is a form of complex system. Due to its complexity, the determinants within such system are not linked to each others in a linear, predictable manner only. Predictability is possible only on (...) a short-term basis, and unpredictability sets in over the long run. Understanding such a system of determinants calls for articulation and testing of complex models which synthesize our knowledge of multiple determinants at many scales, both biological and otherwise. Given the complexity of this web and existing knowledge about the nonlinearity of such systems, the following question is posed: Can the challenge of studying causality be adequately addressed if emphasis continues to be placed on using tools and methods that are geared towards looking at such system from a linear paradigm? Or is it time to add to the epidemiologic research agenda the notion of nonlinearity and its relevant form of analytical approaches that are being tested in other disciplines? Furthermore, the question posed here applies as well to the study of determinants of health. Addressing determinants of heath adds further complexity to our task. (shrink)

Although it is now generally acknowledged that new biomedical technologies often produce new definitions and sometimes even new concepts of disease, this observation is rarely used in research that anticipates potential ethical issues in emerging technologies. This article argues that it is useful to start with an analysis of implied concepts of disease when anticipating ethical issues of biomedical technologies. It shows, moreover, that it is possible to do so at an early stage, i.e. when a technology is (...) only just emerging. The specific case analysed here is that of ‘molecular medicine’. This group of emerging technologies combines a ‘cascade model’ of disease processes with a ‘personal pattern’ model of bodily functioning. Whereas the ethical implications of the first are partly familiar from earlier—albeit controversial—forms of preventive and predictive medicine, those of the second are quite novel and potentially far-reaching. (shrink)

The “Asian disease” problem (Tversky & Kahneman, 1981) demonstrated behaviour in contradiction to the invariance axiom of EU theory. However, the risky choice behaviour was simply seen by the equate-to-differentiate model as a choice between the best possible outcomes or a choice between the worst possible outcomes. It was then argued that a way in which frame influences choice is through the perceived difference between possible outcomes. A “judgement” task was designed to examine whether the knowledge of “the (...) value difference between each possible outcome and the certain outcome” will permit prediction of preference in the choice pattern related to the Asian disease problem. Participants were exposed to an anthrax disease problem (the original or probabilistic version of the Asian disease problem) and a SARS problem (the fuzzy version of the Asian disease problem). It was shown that the empirical evidence in relation to the Asian disease problem could be satisfactorily accounted for by the generalised weak dominance strategy revealed by the judgement results. (shrink)

The paper deals with the use of predictive genetic tests in medical research. I limit my discussion to those advances in genetics which try to overcome the limits represented by our genetic make-up, in particular by gene mutations that lead, or could lead, to the development of genetic diseases. Besides the ethical issues concerning the topic of the current discussion, the reader will also find an evaluation of the legal provisions elaborated at the different levels of the legal order. The (...) aim of this evaluation is to find out which model of Law is being adopted in bioethical issues like the one discussed in this paper. The paper underlines and argues how Law can contribute to value and to spread an ethics of responsibility. Tekst se bavi primenom prediktivnih analiza u medicinskim istrazivanjima. Diskusija se odnosi samo na one vidove genetike kojima se pokusavaju prevazici ogranicenja sadrzana u nasem genetskom sklopu, pre svega ogranicenja koja su izazvana mutacijama gena koja izazivaju ili mogu izazvati pojavu genetskih oboljenja. Pored etickih pitanja vezanih za temu ove diskusije, tekst razmatra i pravna resenja s razlicitih nivoa pravog poretka. Cilj procene pravnih resenja je da utvrdi koji je pravni model usvojen u slucaju bioetickih pitanja kojima se bavi ovaj tekst. U tekstu se naglasava da zakon moze doprineti afirmisanju i sirenju etike odgovornosti. (shrink)

Hypertension is a common disease in highly industrialized societies, more often perceived as a health problem in adults rather than children. However, epidemiologists are currently paying more attention to the possibility of idiopathic hypertension during childhood. This article compares three classification models (logistic regression, classification trees and MARSplines) in order to determine the best classification model and distinguish the parameters that are most important in the detection of abnormal blood pressure in children. The study group consisted of 1,378 (...) children aged between 7 and 18. After making comparisons between the methods, it was determined that MARSplines is the model that best assigns subjects to classes and can be an alternative in cases when traditional statistical methods cannot be used due to a lack of fulfillment of conditions. For prediction of abnormal blood pressure in this age group, the most important parameters were the heart rate and selected indicators of body proportions. (shrink)

The semantic fluency task is a widely used clinical tool in the diagnostic process of Alzheimer's disease. The task requires efficient mapping of the semantic space to produce as many items as possible within a semantic category. We examined whether healthy volunteers and patients with early Alzheimer's disease take advantage of and travel in the semantic space differently. With focus on the animal fluency task, we sought to emulate the detailed structure of the multidimensional semantic space by utilizing (...) word2vec-method from the natural language processing domain. To render the resulting multidimensional semantic space visually comprehensible, we applied a dimensionality reduction algorithm, which enabled a straightforward division of the semantic space into sub-categories. Moving in semantic space was quantified with the number of items created, sub-categories visited, and switches and returns to these sub-categories. Multinomial logistic regression models were used to predict the diagnostic group with these independent variables. We found that returning to a sub-category provided additional information, besides the number of words produced in the task, to differentiate patients with Alzheimer's dementia from both amnestic Mild Cognitive Impairment patients and healthy controls. The results suggest that the frequency of returning to a sub-category may serve as an additional aid for clinicians in diagnosing early Alzheimer's disease. Moreover, our results imply that the combination of word2vec and subsequent t-SNE-visualization may offer a valuable tool for examining the semantic space and its sub-categories. (shrink)

Our time is marked by a resurgence of hate that threatens to increase oppression. Social media has contributed to this by acting as a medium through which hate speech is spread. How should we model the spread of hate? This article considers two models. First, I consider a simple contagion model. In this model, hate spreads like a virus through a social network. This model, however, fails to capture the fact that people do not acquire hatred (...) from a single infectious contact. Instead, it builds up in a person's beliefs and attitudes through time until the infection reaches a level where the subject themselves becomes a generator of hate speech. Second, to accommodate this, I consider an alternative model known as complex contagion. I argue that not only is a complex contagion model more explanatory and predictive, but it can be used to explain why certain features of social media cause it to be a promoter of hate. I conclude by sketching some mitigation strategies. (shrink)