{"id":"W2920854314","doi":"10.2196/13039","title":"Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electronic health record; Computer science; Health records; Natural language processing; Medicine; Data science; Artificial intelligence; Health care","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02739457,0.001409006,0.000898006,0.004899306,0.0007424014,0.001492573,0.001380066,0.001094563,0.001070462],"category_scores_gemma":[0.07048524,0.0004700009,0.001620863,0.002532312,0.00063602,0.00250982,0.001267686,0.001363451,0.0006673889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001283419,"about_ca_system_score_gemma":0.002642923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008108163,"about_ca_topic_score_gemma":0.006909627,"domain_scores_codex":[0.9875655,0.008150551,0.001219603,0.001743401,0.001137886,0.0001830683],"domain_scores_gemma":[0.8198663,0.1672659,0.003111517,0.002856707,0.006456627,0.0004428977],"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":[0.001485963,0.0009622526,0.1066504,0.001304755,0.0009412349,0.000575119,0.001253237,0.07584711,0.008006824,0.001312674,0.009220132,0.7924403],"study_design_scores_gemma":[0.0001855578,0.0006609448,0.03404051,0.0001884176,0.0003391694,0.000519946,0.0004651452,0.9503389,0.006996354,0.003264787,0.002904593,0.00009570437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3459291,0.002751651,0.6347737,0.002375285,0.0002021007,0.001815042,0.00415629,0.006234278,0.001762496],"genre_scores_gemma":[0.4898349,0.0007404367,0.5019712,0.0003817953,0.0001338202,0.0008101503,0.005458428,0.0001729139,0.0004963525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02739457,"threshold_uncertainty_score":0.1448781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02793280892104718,"score_gpt":0.4884979843642453,"score_spread":0.4605651754431981,"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."}}