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Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.
van de Burgt, Britt W M; Wasylewicz, Arthur T M; Dullemond, Bjorn; Jessurun, Naomi T; Grouls, Rene J E; Bouwman, R Arthur; Korsten, Erik H M; Egberts, Toine C G.
Afiliación
  • van de Burgt BWM; Division of Clinical Pharmacy, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.
  • Wasylewicz ATM; Division Healthcare Intelligence, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.
  • Dullemond B; Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands.
  • Jessurun NT; Division Healthcare Intelligence, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.
  • Grouls RJE; Department of Mathematics and Computer Science, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands.
  • Bouwman RA; Netherlands Pharmacovigilance Centre LAREB, 5237 MH 's-Hertogenbosch, The Netherlands.
  • Korsten EHM; Division of Clinical Pharmacy, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands.
  • Egberts TCG; Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, 5612 AP Eindhoven, The Netherlands.
JAMIA Open ; 7(3): ooae070, 2024 Oct.
Article en En | MEDLINE | ID: mdl-39156048
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: JAMIA Open Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos
Texto completo
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Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: JAMIA Open Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos