Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.

Autor:	van de Burgt BWM; Division of Clinical Pharmacy, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.; Division Healthcare Intelligence, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.; Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands., Wasylewicz ATM; Division Healthcare Intelligence, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands., Dullemond B; Department of Mathematics and Computer Science, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands., Jessurun NT; Netherlands Pharmacovigilance Centre LAREB, 5237 MH 's-Hertogenbosch, The Netherlands., Grouls RJE; Division of Clinical Pharmacy, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands., Bouwman RA; Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands.; Department of Anesthesiology, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands., Korsten EHM; Division Healthcare Intelligence, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.; Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands., Egberts TCG; Department of Clinical Pharmacy, University Medical Centre Utrecht, 3584 CX Utrecht, The Netherlands.; Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, 3584 CX Utrecht, The Netherlands.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2024 Aug 16; Vol. 7 (3), pp. ooae070. Date of Electronic Publication: 2024 Aug 16 (Print Publication: 2024).
DOI:	10.1093/jamiaopen/ooae070
Abstrakt:	Objective: Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs. Materials and Methods: In Phase I, our previously developed CDSS algorithm was recoded and improved upon with the same relatively large dataset of 35 000 notes (Step A), using R to identify possible ADRs with Medical Dictionary for Regulatory Activities (MedDRA) terms and the related Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) (Step B). In Phase II, 6 existing text-mining R-scripts were used to detect and present unique ADRs, and positive predictive value (PPV) and sensitivity were observed. Results: In Phase IA, the recoded algorithm performed better than the previously developed CDSS algorithm, resulting in a PPV of 13% and a sensitivity of 93%. For The sensitivity for serious ADRs was 95%. The algorithm identified 58 additional possible ADRs. In Phase IB, the algorithm achieved a PPV of 10%, a sensitivity of 86%, and an F-measure of 0.18. In Phase II, four R-scripts enhanced the sensitivity and PPV of the algorithm, resulting in a PPV of 70%, a sensitivity of 73%, an F-measure of 0.71, and a 63% sensitivity for serious ADRs. Discussion and Conclusion: The recoded Dutch algorithm effectively identifies ADRs from free-text Dutch EHRs using R-scripts and MedDRA/SNOMED-CT. The study details its limitations, highlighting the algorithm's potential and significant improvements. Competing Interests: The authors have no competing interests to declare. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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