By David Riaño, Richard Lenz, Manfred Reichert
This ebook constitutes the refereed lawsuits of 2 workshops held on the foreign convention on well-being – Exploring Complexity and clinical Informatics Europe, HEC 2016, held in Munich, Germany, in September 2016: the eighth overseas Workshop on wisdom illustration for wellbeing and fitness Care, KR4HC 2016, and the ninth foreign Workshop on Process-oriented details structures in Healthcare, ProHealth 2016.
The eight revised complete papers have been conscientiously reviewed and chosen from 12 submissions. The papers are geared up in topical sections on ontologies in well-being care; scientific caliber, overview, and simulation; desktop guidance engineering and utilization; and comorbidity and medical technique management.
Read Online or Download Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers PDF
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Extra info for Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers
The Treatment Knowledge. Clinical practice guidelines contain therapeutic indica‐ tions that Clinical Pathways adapt to concrete clinical contexts. , the Anatomical Therapeutic Chemical Classiﬁcation (ATC) deﬁnes ﬁve levels of detail for drugs, the ATC Deﬁned Daily Doses (DDD) describes the average dose per day for drugs in adults, and the ICD-10-PCS provides a classiﬁcation of medical procedures in seven abstraction levels). Decision tables are knowledge representation structures that have been previously used to describe clinical procedures [8, 11, 15, 16].
2), and accumulates the total time the patient has remained in this stage. This information is used to assess whether the patient can or cannot evolve to a new stage as an eﬀect of an exacerbation, a worsening, or a regular evolution as it was discussed in the previous section. Broadly speaking, there are two main actions on the virtual patient: generation and evolution. 44 D. Riaño and A. Fernández-Pérez Virtual Patient Generation. New patients can be generated from the information contained in the Knowledge Level.
Exclusion criteria Identify the exclusion criteria from the constrains defined in previous steps No constraint was an exclusion criterion 9. 4 Evaluation We adopted three diﬀerent methods to evaluate our results. Firstly, the computed results were analyzed based on widely adopted Chinese clinical guidelines to check whether there were obviously erroneous results. Subsequently, the computed results were compared to Dentler’s original results based on Dutch patient data to analyze their correlation.