{"doi":"10.1093/jamiaopen/ooaf090","title":"Benchmarking of pre-training strategies for electronic health record foundation models","abstract":"Objective: Our objective is to compare different pre-training strategies for electronic health record (EHR) foundation models. Materials and Methods: We evaluated three approaches using a transformer-based architecture: baseline (no pre-training), self-supervised pre-training with masked language modeling, and supervised pre-training. The models were assessed on their ability to predict both major adverse cardiac events and mortality occurring within 12 months. The pre-training cohort was 405 679 patients prescribed antihypertensives and the fine tuning cohort was 5525 patients who received doxorubicin. Results: Task-specific supervised pre-training achieved superior performance (AUROC 0.70, AUPRC 0.23), outperforming both self-supervised pre-training and the baseline. However, when the model was evaluated on the task of 12-month mortality prediction, the self-supervised model performed best. Discussion: While supervised pre-training excels when aligned with downstream tasks, self-supervised approaches offer more generalized utility. Conclusion: Pre-training strategy selection should consider intended applications, data availability, and transferability requirements.","journal":"JAMIA Open","year":2025,"id":526516,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9374,"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":1402626,"name":"Shreya D’Souza","orcid":null,"position":1,"is_corresponding":false},{"id":1021369,"name":"David Seong","orcid":"0000-0002-8980-5731","position":2,"is_corresponding":false},{"id":952035,"name":"Eloïse Berson","orcid":"0000-0003-1046-125X","position":3,"is_corresponding":false},{"id":278117,"name":"Camilo Espinosa","orcid":"0000-0003-1630-1564","position":4,"is_corresponding":false},{"id":226949,"name":"Nima Aghaeepour","orcid":"0000-0002-6117-8764","position":5,"is_corresponding":false},{"id":17263,"name":"Samson Mataraso","orcid":"0000-0003-3146-2243","position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":null,"created_at":"2026-07-19T02:50:30.402772Z","pmid":"40809468","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":[]}