Catalina Martínez-Costa1, Ronald Cornet2, Daniel Karlsson3, Stefan Schulz4, Dipak Kalra5. 1. Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria catalina.martinez@medunigraz.at. 2. Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands catalina.martinez@medunigraz.at. 3. Department of Biomedical Engineering, University of Linköping, Linköping, Sweden. 4. Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria. 5. Centre for Health Informatics & Multiprofessional Education, University College London, London, UK.
Abstract
OBJECTIVE: To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information. MATERIALS AND METHODS: Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling. We applied this approach to heterogeneous representations of an excerpt of a heart failure summary. RESULTS: Using a set of finite top-level patterns to derive semantic patterns, we demonstrate that those patterns, or compositions thereof, can be used to represent information from clinical models. Homogeneous querying of the same or similar information, when represented according to heterogeneous clinical models, is feasible. DISCUSSION: Our approach focuses on the meaning embedded in EHRs, regardless of their structure. This complex task requires a clear ontological commitment (ie, agreement to consistently use the shared vocabulary within some context), together with formalization rules. These requirements are supported by semantic patterns. Other potential uses of this approach, such as clinical models validation, require further investigation. CONCLUSION: We show how an ontology-based representation of a clinical summary, guided by semantic patterns, allows homogeneous querying of heterogeneous information structures. Whether there are a finite number of top-level patterns is an open question.
OBJECTIVE: To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information. MATERIALS AND METHODS: Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling. We applied this approach to heterogeneous representations of an excerpt of a heart failure summary. RESULTS: Using a set of finite top-level patterns to derive semantic patterns, we demonstrate that those patterns, or compositions thereof, can be used to represent information from clinical models. Homogeneous querying of the same or similar information, when represented according to heterogeneous clinical models, is feasible. DISCUSSION: Our approach focuses on the meaning embedded in EHRs, regardless of their structure. This complex task requires a clear ontological commitment (ie, agreement to consistently use the shared vocabulary within some context), together with formalization rules. These requirements are supported by semantic patterns. Other potential uses of this approach, such as clinical models validation, require further investigation. CONCLUSION: We show how an ontology-based representation of a clinical summary, guided by semantic patterns, allows homogeneous querying of heterogeneous information structures. Whether there are a finite number of top-level patterns is an open question.
Authors: William D Duncan; Thankam Thyvalikakath; Melissa Haendel; Carlo Torniai; Pedro Hernandez; Mei Song; Amit Acharya; Daniel J Caplan; Titus Schleyer; Alan Ruttenberg Journal: J Biomed Semantics Date: 2020-08-20
Authors: Antje Wulff; Marcel Mast; Marcus Hassler; Sara Montag; Michael Marschollek; Thomas Jack Journal: Methods Inf Med Date: 2020-10-14 Impact factor: 2.176
Authors: Blanda Helena de Mello; Sandro José Rigo; Cristiano André da Costa; Rodrigo da Rosa Righi; Bruna Donida; Marta Rosecler Bez; Luana Carina Schunke Journal: Health Technol (Berl) Date: 2022-01-26