Ontology Based Annotation of Contextualized Vital Signs Albert Goldfain1,* Min Xu1, Jonathan Bona2, and Barry Smith2 1 Blue	Highway	Inc.,	Syracuse,	NY,	USA 2	University	at	Buffalo,	Buffalo,	NY,	USA ABSTRACT Representing	the	kinetic	state	of	a	patient	(posture,	motion,	and	ac‐ tivity)	during	vital	sign	measurement	is	an	important	part	of	continu‐ ous monitoring applications, especially remote monitoring applica‐ tions. In contextualized vital sign representation, the measurement result is presented in conjunction with salient	measurement context metadata.	We present an automated annotation system for vital sign measurements	that	uses	ontologies	from	the	Open	Biomedical	Ontolo‐ gy	Foundry (OBO	Foundry) to represent the	patient's kinetic state at the time of measurement. The annotation system is applied to data generated	by	a	wearable	personal	status	monitoring	(PSM)	device.	We demonstrate	how	annotated	PSM	data can	be	queried for contextual‐ ized	vital	signs	as	well	as	sensor	algorithm	configuration	parameters. 1 INTRODUCTION Vital sign measurements are often obtained without close clinical supervision. In hospital settings, ambulatory patient monitoring devices are used to track vital signs when a patient is away from the bedside [1]. Telemedicine applications permit a patient to take readings from a location that is remote to their provider [2]. The availability of consumergrade devices coupled with easy-to-use, web-based health portals has fueled the adoption of vital signs monitoring as part of the Quantified-Self movement [3]. Users can now independently collect various sorts of data for fitness, health, wellness, and disease prevention. What is often lost in these scenarios, relative to a clinically supervised encounter, is an interpretation of the user's context of measurement. As remote continuous vital signs monitoring becomes a reality, the quality of vital signs data will increasingly rely on accurately inferring and representing measurement context in an automated way. We use the term contextualized vital sign for the aggregate of a vital sign and some non-trivial aspect of its measurement context. Paradigmatic contextualized vital signs include: night-time blood pressure, post-operative blood pressure, resting respiratory rate, premenopausal body temperature, and reclining heart rate. Such descriptions are often applied to snapshot (episodic) measurements, and efficiently recorded and transmitted. This paper presents a representation of contextualized vital signs that uses ontologies from the Open Biomedical Ontology (OBO) Foundry. We then use this representation in an automated annotation system for a personal status monitoring (PSM) device data stream. We have developed the PSM system to classify motion, body position, and highacceleration events (such as falls) alongside vital sign measurements. The specific example we use throughout is the * To whom correspondence should be addressed: agoldfain@blue-highway.com representation of a user's body position during a pulse rate measurement. However, the annotation system can scale to cover the entire suite of classifiers. The utility of having ontologically annotated PSM data is manifested in several applications:  Maintaining sensor configuration for each classification.  Maintaining classification algorithm configuration.  Training set construction from annotated PSM data for data mining and machine learning.  Querying PSM results using annotations as criteria.  Describing semantic alarms for continuous monitoring applications [11]. These are discussed below along with potential extensions to the system. 2 BACKGROUND 2.1 Personal Status Monitoring System Accelerometers are the most prevalent sensors used for body-position classification applications [12]. The PSM device is a wearable multi-sensor system consisting of fourteen tri-axial accelerometers and multiple vital sign monitors, each of which is unobtrusive and noninvasive for the user. The accelerometers are mounted in such a way as to minimize noise and are positioned at the hips (2), knees (2), shins (2), shoulders (2), forearms (2), wrists (2), chest (1), and head (1). Four unsupervised classification algorithms are applied to PSM data in order to infer user motion, body position, device orientation, and fall events. Each of these classifications relies on either acceleration measured at each sensor or data derived from the combination of such measurements. For example, body position is inferred using a classifier that takes as input the relative angles between limbs (Euler angles) or (in simple cases) the tilt of a limb relative to the anatomical axes. When all of the accelerometers are used in the classification of body position, the result can be visualized as a rough skeletal wire-frame configuration. Only a subset of the accelerometers is typically required to accurately classify crude body positions such as "sitting", "standing", and "lying down". For clinical applications, one or two active accelerometers will suffice. Vital sign monitors include a heart rate and respiration rate monitor mounted on the chest. Figure 1 illustrates three different embodiments of the PSM device. G 2 r w ti u q e c a p 2 O v      2 O ta e a c ti te to m H p b in s O w c oldfain et al. Figure 1. Mil PSM data a unning data ca orkflow for m on. 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Commonly required in the last trimester of pregnancy to relieve aortocaval compression." Such free-text contextual descriptions are valuable, but to fully realize their value, they must be annotated and linked to machine-readable representations outside of the EHR itself. 3 ONTOLOGY FOR CONTEXTUALIZED VITAL SIGNS We use OBO Foundry ontologies for our annotation system for several reasons: such ontologies are open-source, actively developed by domain experts, use stable IRIs to denote types, honor the distinction between individuals and universals, share the Basic Formal Ontology (BFO) as a common upper-ontology3, and share the OBO Relation Ontology (RO) as a common source for relations [7]. OBO Foundry ontologies are implemented in machine-readable formats (OWL-DL and OBO Format), and are developed to maximize reuse of terms and relations. OBO Foundry reference ontologies are general enough for use across several domains. These are in contrast to application ontologies, which import terms and relations from reference ontologies and define new application-specific terms and relations for the purposes of a given application. We have developed the Ontology for Contextualized Vital Signs (OCVS)4 as an application ontology for PSM data annotations. A central feature of OCVS is its use of external terms and relations from OBO Foundry ontologies when possible. These external terms are used to form cross-product definitions and description logic restrictions. For example standing pulse rate can be defined using a necessary and sufficient DL-restriction using the Vital Sign Ontology (VSO) [8], the Ontology for Biomedical Investigations (OBI) [9], and the Experimental Conditions Ontology (XCO) [10]: "The pulse rate of an organism in the standing position" vso:'pulse rate' AND inheres_in SOME (obi:organism AND bearer_of SOME xco:'standing position') The relations (in bold) are standard relations from the OBO Relation Ontology. OCVS does not have to redefine new terms in order to construct the definition of 'standing pulse rate', and the same cross-product template can be used for different vital signs (from VSO) and body positions 3 http://www.ifomis.org/bfo/ 4 http://www.awqbi.com/ontologies/ocvs.owl (from XCO). OCVS imports terms from the Unit Ontology5 (UO) to represent measurement units. All terms are imported using the MIREOT mechanism [5]. Throughout the paper, the source ontology for a term will be indicated via its OBO prefix (e.g., obi:'measurement datum' is the term 'measurement datum' from the Ontology for Biomedical Investigations). 3.1 Representing Measurements These imported terms are combined with relations from the Relation Ontology to form the basic representations for PSM measurements. A PSM measurement datum consists of three acceleration magnitude measurements, three tilt measurements (relative to each device axis), a signal vector magnitude measurement (SVM), a signal magnitude area (SMA) measurement, and a time stamp representing an interval. The time stamp represents the total running time in seconds from the beginning of the data acquisition session. Acceleration is given in g-units (1 g = 9.8 m/s2), which OCVS asserts to be a type of acceleration unit. The angle of tilt, relative to the acceleration along each axis a, is computed as follows: asin 180 1 This produces an angular measurement of stationary tilt in the range [-90, 90] degrees. SVM is computed with each reading as a function of all three acceleration components at a particular time (x(t), y(t), z(t)): , , 2 The SMA is a running total of the absolute sum of component-wise accelerations over a window of N readings: , , 1 | | | | | | 3 At any given time, the SMA is a sum over a window containing the last N readings. In OCVS, we assert that each of these measurement data is a part of the 'PSM measurement datum' with the same timestamp. There are multiple ways of measuring qualities such as tilt. OCVS includes term annotations indicating the formulas used to derive relevant measurement data, thus providing metadata for consumers of annotated data as to how each input parameter to the body position classifier was derived. 'PSM Measurement Datum' in OCVS is a defined class. Defined classes, like universals, correspond to OWL classes. OWL object properties are used to implement OBO Foundry relations, and OWL data properties are used to link particulars (OWL individuals) to data. The parts of a PSM measurement datum and relevant relations are shown in Figure 2. A single PSM measurement datum is the input to the body position classification algorithm which has as output a 5 http://code.google.com/p/unit-ontology/ G 4 b O p c ' T th O 3 O b th ti in d te s le tw c I a ti oldfain et al. ody position CVS using th Figure 2. Parts o for a sin Currently, t osition terms ally significan decubitus posi hese are often e scope of OC CVS' represe Figure 3. 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T uration to di arameters suc S TO MINIMAL SE r Set Description eter AND tomic_configuratio eter AND tomic_configuratio LY ('right hip' OR eter AND tomic_configuratio eter AND tomic_configuratio LY ('right hip' OR ints, we can c ition classific ve accelerome ithms. ation ontolog ans for an acc guration. We c when: r' ty' by a, along th or the accel ormation of) e assert that eter' (our cho meter', and th ent device'. W MA) Ontolog subset of FMA application. ly proximal c measured valu sentative of th which they ar olds between ent of c1 is ta would be (if c evant anatomi n the accelero that part of th sor. The OCV atomical plane 'transverse p axes are rela em must under ntation of acce he relation fferentiate it h as sampling NSOR SETS n ONLY chest n 'left hip') n ONLY chest n 'left hip') heck if the ation algoters before ically, we elerometer will assert e device x, eration of the corre- 'Freescale sen accelat 'triaxial e use the y to repreterms is The OBO ontinuants. es coming e accelerae attached. continuant ken to de2 were dical entities meter, the e anatomiS also ims ('coronal lane') and tive to the go a translerometers is labeled from other rate. Ontology Based Annotation of Contextualized Vital Signs 5 Finally, we express the relationship between measurement data and the measurement devices that produce them. We assert dt measured_using dev when: 1. dt is a obi:'measurement data' 2. dev is a obi:'measurement device' 3. dt is the specified output of the realization of dev's measure function. This allows us to make assertions linking PSM device types to data types. The universal-level relation expresses the link for all such device and data pairings. This implies that all particular instances of datum universals are linked to instances of device universals. This is important because the PSM has fourteen accelerometer instances and an arbitrary number of (potentially redundant) vital sign sensor instances. For example, if we want to measure a pulse rate using both a pulse oximeter and a cardiac monitor, the same data type can be linked via the universal-level restriction: vso:'pulse rate measurement datum' rdfs:subClassOf measured_using SOME ('pulse oximeter' OR 'cardiac monitor') The measurement device types are then linked to the measure functions they were designed to realize: 'triaxial accelerometer' rdfs:subClassOf has_function ONLY 'acceleration magnitude measure function' 'pulse oximeter' rdfs:subClassOf has_function SOME 'pulse rate measure function' Notice that the quantifiers differ here. The only function of the accelerometer is to measure acceleration magnitude, however the measure function of the pulse oximeter can be realized in multiple processes (including blood oxygen measurement processes and pulse rate measurement processes). The ontology captures multi-function devices via different measure function types. The corresponding instance-level relations (denoted in italics: measured_using) calls out the particular devices used. For example: (1.1g, 0.6g, -0.2g) measured_using Freescale MMA7660FC Triaxial Accelerometer #6. Two pulse rate measurement data instances can differ and still be of the same ontological type. The provenance of each datum is captured by the instance-level relation that ties it to a particular device, allowing for redundant readings. 3.3 Vital Sign Monitoring Device Configuration Pulse rate and heart rate are typically highly correlated and are often used interchangeably. The PSM uses an on-garment chest-strap cardiac monitor to compute heart rate. However, the PSM can also be configured to use a clip-on pulse oximeter for pulse rate measurements. In order to capture the provenance of vital sign measurement data, OCVS represents: the device, the vital sign being measured, and the anatomic configuration of the monitoring device. Superclass terms for 'vital sign measurement device' and 'pulse rate measurement datum' are drawn from the Vital Sign Ontology. Anatomic configuration for vital sign monitoring devices is represented in the same way as accelerometer anatomic configuration, only without the need for the orientation or proxy measurement of condition 4. 4 AUTOMATED ANNOTATION SYSTEM OCVS was developed to facilitate automated annotation of PSM data. Annotation involves associating each PSM configuration parameter, numerical measurement, and classifier prediction with an OCVS term (i.e., making an assertion about an owl:NamedIndividual using rdf:type), and asserting instance-level relations that hold between particular individuals. The resulting output can be expressed using the Resource Description Framework (RDF) and serialized using a suitable RDF syntax. RDF Turtle syntax is preferred for transmission to minimize file size. We use the SPARQL query language to query annotated PSM data files (see next section). An example of a part of an annotated PSM measurement datum is shown in Table II. TABLE II. PART OF AN ANNOTATED PSM MEASUREMENT DATUM Reading #13384_2 part_of (BFO_0000051) instance-level assertions :13384_2 rdf:type psm:PSM_0000010 , owl:NamedIndividual ; obo:BFO_0000051 :13384_2AccelX , :13384_2AccelY , :13384_2AccelZ ; obo:IAO_0000581 :13384_2ReadingTime ; obo:BFO_0000051 :13384_2SMA , :13384_2SVM , :13384_2TiltX , :13384_2TiltY , :13384_2TiltZ . owl:NamedIndividual rdf:type obo:has_measu rement_value #13384_2ReadingTime time measurement datum (IAO_0000416) 47.00 (s) #13384_2AccelX accelerometer x-axis acceleration magnitude measurement datum (PSM_0000031) 0.19 (g) #13384_2TiltZ accelerometer z-axis tilt measurement datum (PSM_0000038) 0.02 (deg) #13384_2BodyPosMDatum body position measurement datum (PSM_0000029) Upright The annotation system is implemented using a series of PHP scripts that are invoked offline and after a particular session has ended. Each session is given a unique identifier and is assumed to have a stable anatomical and device configuration throughout. Annotated PSM data files6 are only linked to the ontology by way of the IRI identifier of the corresponding OCVS types. This loose coupling allows for further development and refinement of the ontology without changing the representation of the annotated data file. All that is required is that OCVS types retain their IRI identifiers. The representation is also flexible enough to permit arbitrarily many new sensors 6 See http://www.awqbi.com/ontologies/psm-instances.owl for a small sample annotated data file. Goldfain et al. 6 to be integrated into the PSM platform without invalidating previously annotated (legacy) PSM data. 5 QUERYING ANNOTATED PSM DATA We utilize the SPARQL to query annotated RDF-formatted PSM data. Annotated PSM data is queried locally using the ARQ command-line tool from the Apache Jena framework. The following is part of a SPARQL query that returns all of the PSM measurement data measured using an accelerometer positioned at the sternum in which the inferred body position is 'Bending Backward': SELECT DISTINCT ?psmmd WHERE { ?psmdevtype rdfs:label "PSM device"@en . ?cfg rdfs:label "Sternum"@en . ?psmmdt rdfs:label "PSM measurement datum"@en . ?d rdf:type ?psmdevtype . ?d part_of: ?psmdevpart . ?psmdevpart has_anatomic_configuration: ?cfg . ?psmmd measured_using: ?d . ?psmmd rdf:type ?psmmdt . ?alg has_specified_input: ?psmdt . ?alg has_specified_output: ?bpmd . ?bpmd has_body_position_measurement: "Bending Backward"^^rdfs:Literal . } The query results are bound to ?psmmd and represent PSM measurement data that satisfy the criteria in the WHERE clause. This query exemplifies several different search criteria we may apply to the annotated PSM data set. If we are interested in the details of the configuration (e.g., the devices used, their sampling rates, and their anatomical configurations), then we could expand the query on the results bound to ?psmdevpart. If we are interested in the contextualized vital sign measurement value, we can expand the query on the results bound to ?psmmd and examine its measurement data parts. If we want to obtain details about the algorithm configuration, we can examine ?alg. From a user interface perspective, it is easier to provide a web-based form from which queries can be constructed. We are implementing scripts to programmatically generate queries via the Graphite PHP Linked Data library7. 6 CONCLUSION OCVS provides a representation of vital sign measurement context using the OBO Foundry ontologies. On the strength of cross-product definitions from orthogonal, independently developed ontologies, we are able to create descriptions of body positions, configurations, and queries in a compositional way. OCVS metadata captures enough domain knowledge to serve as a meaningful component of a pattern classification pipeline. The semantic web standards used to build our annotation system enable decentralized development, storage, and query of resources. Further development on OCVS (or any of the OBO Foundry ontologies on which it relies) will not disrupt the data acquisition and classification routines. OCVS is currently used to annotate continuous raw sensor measurement data. As such, the annotated PSM data is at the finest granularity. Currently, such data only need to be transmitted when an episodic reading is taken. In applications requiring more continuous transmission, OCVS-based 7 http://graphite.ecs.soton.ac.uk/ annotation can be applied to more coarse-grained data such as feature sets or sets of classifier outputs. A switch in data granularity will only require extension of the ontology rather than a switch of ontologies. We believe that using OBO Foundry ontologies and semantic web standards can serve as the core knowledge representation for contextualized vital signs. Such a representation can be extended to perform further contextualization (e.g., disease-based contextualization) depending on the requirements of the particular application. ACKNOWLEDGEMENTS We would like to acknowledge the useful comments of three anonymous reviewers. REFERENCES [1] Davenport, D. M., Deb, B., and Ross, F. J. (2009), Wireless Propagation and Coexistence of Medical Body Sensor Networks for Ambulatory Patient Monitoring, Proc. Body Sensor Networks (BSN2009), IEEE Press, 41–45. [2] Pattichis, C. S. (2002), Wireless telemedicine systems: an overview, Antennas and Propagation, 44(2): 143–153. [3] Swan, M. (2009), Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking, International Journal of Environmental Research and Public Health, 6(2): 492–525. [4] Beale, T. (2003), Archetypes and the EHR, Advanced Health Telematics and Telemedicine, IOS Press, 238–244. [5] Courtot, M., Gibson, F., Lister, A. L., Malone, J., Schober, D., Brinkman, R. R., and Ruttenber, A. (2011), MIREOT: The minimum information to reference an external ontology term, Journal of Applied Ontology, 6(1): 23–33. [6] Smith, B., Ashburnter, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L. J., Eilbeck, K., Ireland, A., Mungall, C. J., OBI Consortium, Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S., Scheuermann, R. H., Shah, N., Whetzel, P. L., and Lewis, S. (2007), The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration, Nature Biotechnology, 25: 1251– 1255. [7] Smith, B., Ceusters, W., Klagges, B., Köhler, J., Kumar, A., Lomax, J., Mungall, C., Neuhaus, F., Rector, A. L., and Rosse, C. (2005) Relations in biomedical ontologies, Genome Biology 6(5): R46. [8] Goldfain, A., Smith, B., Arabandi, S., Brochhausen, M., and Hogan, W. R. (2011), Vital Sign Ontology, Proceedings of the Workshop on Bio-Ontologies, ISMB: 71–74. [9] Brinkman, R. R., Courtot, M., Derom, D., Fostel, J. M., He, Y., Lord, P., Malone, J., Parkinson, H., Peters, B., Rocca-Serra, P., Ruttenberg, A., Sansone, S., Soldatova, L. N., Stoeckert, C. J., Turner, J. A., Zheng, J., and OBI Consortium (2010), Modeling biomedical experimental processes with OBI, Journal of Biomedical Semantics 1(Suppl 1): S7. [10] Shimoyama, M., Nigam, R., McIntosh, L. S., Nagarajan, R., Rice, T., Rao, D. C., and Dwinell, M. R. (2012), Three ontologies to define phenotype measurement data, Frontiers in Genetics 3(87). [11] Goldfain, A., Chowdhury, A. R., Xu, M., DelloStritto, J., and Bona, J. (2011) "Semantic Alarms in Medical Device Networks", Proceedings of Third Joint Workshop on High Confidence Medical Device Software and Systems/Medical Device Plug-and-Play Interoperability. [12] Mannini, A. and Sabatini, A. M. (2010), Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers, Sensors 10: 1154–1175.