{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003365295,0.000158257,0.0006618138,0.0001504754,0.00004778391,0.00003728445,0.0007177818,0.0002060524,0.000175968],"category_scores_gemma":[0.0009484557,0.0001289603,0.00009355125,0.0004346288,0.00008426969,0.0004348701,0.0002248074,0.001347272,0.00001345812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000151451,"about_ca_system_score_gemma":0.001895932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00028885,"about_ca_topic_score_gemma":0.000016533,"domain_scores_codex":[0.9957368,0.0007047218,0.001885725,0.0002040649,0.001045524,0.0004231536],"domain_scores_gemma":[0.9953344,0.002570913,0.001202218,0.0005001661,0.0001348174,0.0002574239],"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":[0.00001221972,0.0001196088,0.01261186,0.001146676,0.00002617789,0.000002647557,0.002766411,0.00001913369,0.00001236132,0.002719607,0.0001165983,0.9804467],"study_design_scores_gemma":[0.000552953,0.0006627348,0.06506623,0.000289235,0.000008268658,0.00002472853,0.0004846013,0.9315625,0.0000140594,0.0003809689,0.0008059586,0.000147784],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2358838,0.0004331622,0.7617699,0.0006467201,0.0005986299,0.0003877558,0.000008385814,0.00008530611,0.0001864022],"genre_scores_gemma":[0.556171,0.00005860936,0.4433298,0.0003410708,0.00004965323,0.00000836205,0.00002450809,0.000006867804,0.00001017908],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9802989,"threshold_uncertainty_score":0.5853299,"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."}}