Structured machine learning: the next ten years
Machine Learning
Representing and reasoning over a taxonomy of part-whole relations
Applied Ontology - Ontological Foundations of Conceptual Modelling
Generating personalized advice for schizophrenia patients
Artificial Intelligence in Medicine
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With the introduction of electronic personal health records and e-health applications spreading, interoperability concerns are of increasing importance to hospitals and care facilities. Interoperability between distributed and complex systems requires, among other things, compatible data formats. The recommended approach is to store data using international terminology standards. For data that is not stored in this way, a conversion process must happen. This can be tedious manual work when multiple input and output formats are to be supported. We present WEGWEIS, a web application for schizophrenia patients that converts questionnaire answers into advice. The system's advice delivery is based on data extracted from the electronic medical records of 1379 patients. In WEGWEIS, we handle the conversion by decoupling input formats from output formats, using an ontology as intermediate layer. We present the algorithm and provide details on its implementation.