Abstract:
Linking computational physiology models and simulations with real-world health data, including both clinical and wellness type which may also include environmental factors such as nutrition, physical exercise etc., can result in more elaborate, better validated and more robust computational models. This would lead to more personalized and predictive clinical decision support systems and ultimately help realize Precision Medicine. However this can be hindered by syntactic and semantic inconsistencies. Standards such as openEHR, HL7 and DICOM can address these issues in health information systems, and in a similar way, standards such as CellML, SED-ML, and FieldML can cope with syntactic and semantic inconsistencies in computational models and simulations. This paper proposes a semantically-enriched linkage of computational models and real world health information through a collaborative ontology-based infrastructure. A collaborative shared ontology-based approach is suggested to tackle difficulties in ontology mapping in the first step which can bridge computational models/simulations and health information.