{"doi":"10.1093/jamiaopen/ooaf026","title":"A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records","abstract":"Objectives: Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data. Materials and Methods: = 6622) from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Model performance was evaluated based on the area under the receiver operating characteristic (AUC) and compared with Epic Deterioration Index (EDI), random forest (RF), and extreme gradient boosting (XGBoost). Results: In the COVID-19 development dataset, TECO achieved higher AUC (0.89-0.97) across various time intervals compared to EDI (0.86-0.95), RF (0.87-0.96), and XGBoost (0.88-0.96). In the 2 MIMIC testing datasets (EDI not available), TECO yielded higher AUC (0.65-0.77) than RF (0.59-0.75) and XGBoost (0.59-0.74). In addition, TECO was able to identify clinically interpretable features that were correlated with the outcome. Discussion: The TECO model outperformed proprietary metrics and conventional machine learning models in predicting ICU mortality among patients with COVID-19, widespread inflammation, respiratory illness, and other organ failures. Conclusion: The TECO model demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.","journal":"JAMIA Open","year":2025,"id":512462,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.606,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1021179,"name":"Zifan Gu","orcid":"0000-0002-8024-2629","position":1,"is_corresponding":false},{"id":1138498,"name":"Hongyin Lai","orcid":"0000-0002-8613-5920","position":2,"is_corresponding":false},{"id":704068,"name":"Tanna L. Nelson","orcid":null,"position":3,"is_corresponding":false},{"id":704069,"name":"Tony Keller","orcid":null,"position":4,"is_corresponding":false},{"id":704070,"name":"Clark Walker","orcid":null,"position":5,"is_corresponding":false},{"id":994267,"name":"Kevin W. Jin","orcid":"0000-0002-9217-4803","position":6,"is_corresponding":false},{"id":1371956,"name":"Catherine Chen","orcid":"0000-0002-3367-0655","position":7,"is_corresponding":false},{"id":45212,"name":"Ann Marie Návar","orcid":"0000-0002-6197-9860","position":8,"is_corresponding":false},{"id":704067,"name":"Ferdinand Velasco","orcid":null,"position":9,"is_corresponding":false},{"id":271438,"name":"Eric D. Peterson","orcid":"0000-0002-5415-4721","position":10,"is_corresponding":false},{"id":27515,"name":"Guanghua Xiao","orcid":"0000-0001-9387-9883","position":11,"is_corresponding":false},{"id":342413,"name":"Donghan M. Yang","orcid":"0000-0003-1935-0214","position":12,"is_corresponding":false},{"id":20648,"name":"Yang Xie","orcid":"0000-0003-4293-0014","position":13,"is_corresponding":false},{"id":664826,"name":"Ruichen Rong","orcid":"0000-0002-3205-8915","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-19T02:48:06.263458Z","pmid":"40213364","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}