112768 20240319080953.0 doi 10.2196/30345 sideral 128188 ART-2022-128188 eng Montoto, C. Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records:A Validation Study for the PREMONITION-CD Project 2022 Access copy available to the general public Unrestricted Background: The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs. Objective: The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD. Methods: We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables. Results: The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively. Conclusions: This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish. © 2022 JMIR Medical Informatics. All rights reserved. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.2 2022 MEDICAL INFORMATICS 18 / 31 = 0.581 2022 Q3 T2 0.963 2022 Health Information Management 2022 Q2 Health Informatics 2022 Q2 5.6 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Gisbert, J.P. Guerra, I. Plaza, R. Pajares Villarroya, R. Moreno Almazán, L. López Martín, M. Del Carmen Domínguez Antonaya, M. Vera Mendoza, I. Aparicio, J. Martínez, V. Tagarro, I. Fernandez-Nistal, A. Canales, L. Menke, S. Gomollón, F. Universidad de Zaragoza (orcid)0000-0003-0076-3529 1007 610 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Medicina 10, 2 (2022), e30345 [9 pp.] JMIR med. inform. JMIR medical informatics 2291-9694 234927 http://zaguan.unizar.es/record/112768/files/texto_completo.pdf Versión publicada 1956879 http://zaguan.unizar.es/record/112768/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:112768 articulos driver 2024-03-18-13:16:12 ARTICLE