2026-04-18T04:58:39Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/103562025-10-24T10:58:05Zcom_2072_378068com_2072_378040col_2072_378090

2025-10-24T10:58:05Z urn:hdl:11351/10356 Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review Carrasco Ribelles, Lucía Amalia Llanes Jurado, Jose Gallego Moll, Carlos Cabrera-Bean, Margarita Monteagudo, Mònica Violán-Fors, Concepción Zabaleta-del-Olmo, Edurne Intel·ligència artificial - Aplicacions a la medicina Registres mèdics Històries clíniques - Informàtica PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Investigative Techniques::Epidemiologic Methods::Data Collection::Records::Medical Records::Medical Records Systems, Computerized HEALTH CARE::Health Services Administration::Organization and Administration::Records::Medical Records FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::técnicas de investigación::métodos epidemiológicos::recopilación de datos::registros::registros médicos::sistemas informatizados de historias clínicas::historias clínicas electrónicas ATENCIÓN DE SALUD::administración de los servicios de salud::organización y administración::registros::registros médicos Intel·ligència artificial; Registres sanitaris electrònics; Model predictiu Inteligencia artificial; Registros sanitarios electrónicos; Modelo predictivo Artificial intelligence; Electronic health records; Predictive model Objective: To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes. Methods: This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively. Results: Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model’s performance. Reporting quality was poor, and a third of the studies were at high risk of bias. Conclusions: AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication. 2025-10-24T10:58:05Z 2025-10-24T10:58:05Z 2023-09-26T12:21:44Z 2023-09-26T12:21:44Z 2023-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/11351/10356 Journal of the American Medical Informatics Association; https://doi.org/10.1093/jamia/ocad168 Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess Oxford Scientia