{"id":"W4402405766","doi":"10.23889/ijpds.v9i5.2717","title":"Extracting Social Determinants of Health from Inpatient Electronic Medical Records","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Alberta Health Services","funders":"","keywords":"Health records; Medical record; Electronic health record; Data science; Computer science; Medicine; Health care; Political science; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005567847,0.00009808433,0.0002140495,0.0003871907,0.001712186,0.00008559457,0.00137224,0.0001087123,0.0005821093],"category_scores_gemma":[0.002053747,0.00008988796,0.00005894794,0.0003405391,0.0001124706,0.001713825,0.0003728424,0.0006944889,0.00002728767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006687982,"about_ca_system_score_gemma":0.003292103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003157121,"about_ca_topic_score_gemma":0.002536791,"domain_scores_codex":[0.9961661,0.0002019609,0.001302841,0.0003613291,0.001486604,0.0004811569],"domain_scores_gemma":[0.9978413,0.0005549148,0.0007045869,0.0002199584,0.0005074083,0.0001718618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002369937,0.0002322375,0.3589333,0.0003144723,0.0001144461,0.00001546441,0.01115089,0.0000565789,0.00008264476,0.07579649,0.02132627,0.5317402],"study_design_scores_gemma":[0.001575597,0.0003130148,0.4963567,0.002691079,0.00004621421,0.0000525613,0.003259293,0.2524421,0.00001989117,0.04272172,0.2001131,0.000408839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544175,0.0005590872,0.01059487,0.0124944,0.01927309,0.0005743891,0.001766728,0.00007482309,0.0002450589],"genre_scores_gemma":[0.9956219,0.0001982969,0.001558316,0.0005516497,0.00138215,0.00001282613,0.0005911613,0.0000124171,0.00007131011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5313314,"threshold_uncertainty_score":0.9995875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3398772363008487,"score_gpt":0.5995087051125427,"score_spread":0.2596314688116941,"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."}}