David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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These are the working notes of the workshop on Model-based and Qualitative Reasoning in Biomedicine, which was held during the European Conference on Artiﬁcial Intelligence in Medicine, AIME’03, on 19th October, 2003, in Protaras, Cyprus. The workshop brought together various researchers involved in the development and use of model-based and qualitative reasoning methods in tackling biomedical problems. Much of the biomedical knowledge is essentially model-based, as it is the understanding of the structure and function of biomedical systems that researchers wish to achieve, and this is best done by developing models of these systems. In situations where it may not be appropriate or possible to use quantitative methods, researchers use qualitative approaches. Depending on the biomedical problem concerned, such descriptions may involve causal, temporal and spatial knowledge, possibly of an uncertain nature. Also in the medical management of disorders in patients, qualitative and model-based approaches are being used. For example, systems used for diagnosing disease rely on explicit models of normal or abnormal structure and behaviour (often referred to as ’ﬁrst-principles models’) of the underlying disease process. Qualitative knowledge plays a role in the modelling of disease and treatment processes, including the handling of the uncertainty involved in these processes. Hence, there is little doubt that model-based and qualitative methods ﬁt the biomedical domain really well. However, one of the problems with research in the biomedical ﬁeld is that researchers applying model-based and qualitative-reasoning methods are often closely linked to their application ﬁeld, such as, for example, cell biology or clinical medicine, and ﬁnd it diﬃcult to keep in contact with colleagues doing similar research, but working in a diﬀerent biomedical application ﬁeld. This is even more diﬃcult if the techniques used are also different. For example, researchers involved in Bayesian network research and researchers using qualitative simulation methods hardly exchange views and ideas, despite the fact that their methods have in common that they emphasise representing qualitative biomedical knowledge. It was the aim of this workshop to bring together researchers along the entire spectrum of the biomedical ﬁeld, from health-care research and clinical medicine to human biology, using a variety of methods and techniques, from (qualitative) Bayesian networks and symbolic machine learning, to qualitative simulation..
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