Abstract
Healthcare professionals often need to adapt Clinical Guidelines to obtain personalized treatment plans for their patients. This adaptation is performed according to the patient’s health state, but also takes into account implicit information (e.g., patients’ preferences, local restrictions, etc). It may demand huge efforts, is error prone and the resulting guideline can contain contradictions. In this sense, Computer-Interpretable Guidelines associated with intelligent systems, offer additional support for healthcare professionals throughout the definition of a treatment plan. In this paper, we present an approach using Semantic Web Technologies to evaluate care actions and detect potential contradictions in the personalization process, taking into account the restrictions and needs for adaptation. This approach is based on the HL7 Reference Information Model, the UMLS Semantic Network and is compatible with most of Computer-Interpretable Guidelines formalisms. The prototype supporting our approach is also presented.
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Bonacin, R., Pruski, C., Da Silveira, M. (2012). Careflow Personalization Services: Concepts and Tool for the Evaluation of Computer-Interpretable Guidelines. In: Riaño, D., ten Teije, A., Miksch, S. (eds) Knowledge Representation for Health-Care. KR4HC 2011. Lecture Notes in Computer Science(), vol 6924. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27697-2_6
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DOI: https://doi.org/10.1007/978-3-642-27697-2_6
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