Merging Ontologies and Data from Electronic Health Records

Salvatore Calcagno, Andrea Calvagna, Emiliano Tramontana () and Gabriella Verga
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Salvatore Calcagno: Cyberetna, 95024 Catania, Italy
Andrea Calvagna: Dipartimento di Matematica e Informatica, University of Catania, 95125 Catania, Italy
Emiliano Tramontana: Dipartimento di Matematica e Informatica, University of Catania, 95125 Catania, Italy
Gabriella Verga: Dipartimento di Matematica e Informatica, University of Catania, 95125 Catania, Italy

Future Internet, 2024, vol. 16, issue 2, 1-16

Abstract: The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired. This article presents an approach that accurately relates data in EHRs to ontologies in the medical realm. Thanks to ontologies, clinicians can be assisted when writing or analysing health records, e.g., our solution promptly suggests rigorous definitions for scientific terms, and automatically connects data spread over several parts of EHRs. The first step of our approach consists of converting selected data and keywords from several EHR formats into a format easier to parse, then the second step is merging the extracted data with specialised medical ontologies. Finally, enriched versions of the medical data are made available to professionals. The proposed approach was validated by taking samples of medical records and ontologies in the real world. The results have shown both versatility on handling data, precision of query results, and appropriate suggestions for relations among medical records.

Keywords: Electronic Health Records; HL7; ontology; data integration; data analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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