2026-04-17T05:52:12Zhttps://recercat.cat/oai/requestoai:recercat.cat:11351/103562025-10-24T10:58:05Zcom_2072_378068com_2072_378040col_2072_378090
RECERCAT
author
Carrasco Ribelles, Lucía Amalia
author
Llanes Jurado, Jose
author
Gallego Moll, Carlos
author
Cabrera-Bean, Margarita
author
Monteagudo, Mònica
author
Violán-Fors, Concepción
author
Zabaleta-del-Olmo, Edurne
2025-10-24T10:58:05Z
2025-10-24T10:58:05Z
2023-09-26T12:21:44Z2023-09-26T12:21:44Z2023-09
http://hdl.handle.net/11351/10356
Intel·ligència artificial; Registres sanitaris electrònics; Model predictiuInteligencia artificial; Registros sanitarios electrónicos; Modelo predictivoArtificial intelligence; Electronic health records; Predictive modelObjective: 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.
Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess
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
Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion