{"id":"W4415126726","doi":"10.1093/jamia/ocaf165","title":"FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Universität Duisburg-Essen; Universitätsklinikum Essen","keywords":"Interoperability; Standardization; Bridging (networking); Health care; Resource (disambiguation); Semantic interoperability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007332421,0.002571826,0.001179543,0.002127941,0.0006864786,0.002754766,0.003729429,0.001380142,0.005265286],"category_scores_gemma":[0.02399028,0.0010285,0.003027949,0.001103756,0.0007265717,0.004084594,0.005282569,0.00270234,0.004155474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001748298,"about_ca_system_score_gemma":0.004948648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.021909,"about_ca_topic_score_gemma":0.02418886,"domain_scores_codex":[0.9961225,0.001373927,0.0003410238,0.001165268,0.0007387337,0.0002584849],"domain_scores_gemma":[0.9928513,0.003959727,0.0004265081,0.001577788,0.0008394475,0.0003452835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002314953,0.001162956,0.03913712,0.001333115,0.001352073,0.001258085,0.001608827,0.2197787,0.0129931,0.0184702,0.1528606,0.5477303],"study_design_scores_gemma":[0.000232758,0.0002837662,0.003350306,0.0001764538,0.0001921493,0.0002983876,0.000204032,0.9248019,0.01144444,0.03061414,0.02820851,0.0001932097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03223981,0.001323303,0.7371449,0.004214017,0.0003638343,0.0006312531,0.02318659,0.1956723,0.005223903],"genre_scores_gemma":[0.346798,0.0008171159,0.5764763,0.003473261,0.0002806341,0.001046831,0.0608133,0.006200965,0.004093596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.021909,"threshold_uncertainty_score":0.04356295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01394723324373387,"score_gpt":0.3554960264741158,"score_spread":0.3415487932303819,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}