{"id":"W4390148007","doi":"10.2196/48995","title":"BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Alberta Health Services","funders":"National Institute on Deafness and Other Communication Disorders; Canadian Institutes of Health Research; Cumming School of Medicine, University of Calgary; University of Alberta","keywords":"Computer science; Machine learning; Artificial intelligence; Artificial neural network; Diagnosis code; Medical record; Inpatient care; Medicine; Medical emergency; Deep learning; Retrospective cohort study; Cohort; Health care; Surgery; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003052864,0.0002555293,0.0005604213,0.0001327372,0.0006674524,0.0000187363,0.0003934029,0.0006839848,0.0002091772],"category_scores_gemma":[0.00106993,0.0002139851,0.00015517,0.000667833,0.00009058445,0.0002122526,0.0001655488,0.00173694,0.0003503358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005186777,"about_ca_system_score_gemma":0.0007323837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004024765,"about_ca_topic_score_gemma":0.005039816,"domain_scores_codex":[0.994962,0.0004355746,0.001501536,0.0002604195,0.001766755,0.001073696],"domain_scores_gemma":[0.9973643,0.001008389,0.0005161095,0.0004050331,0.0002263239,0.0004798184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000483314,0.001088218,0.8915702,0.0003768748,0.0002735826,0.00001012616,0.007112084,0.0000218957,0.00000576399,0.0002456204,0.06431343,0.03449895],"study_design_scores_gemma":[0.003836808,0.0009457301,0.7376481,0.0002841263,0.00006255014,8.083035e-7,0.00298626,0.245163,5.562604e-7,0.001248683,0.007589583,0.0002338496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853208,0.00002062916,0.005988148,0.0005489339,0.002459412,0.004353499,0.00005146969,0.0005176688,0.0007394478],"genre_scores_gemma":[0.9915915,0.00008635934,0.0002124291,0.002715307,0.001393992,0.002956256,0.0007520879,0.00004016167,0.0002518892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2451411,"threshold_uncertainty_score":0.8726061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880861001238383,"score_gpt":0.3565630539393577,"score_spread":0.3377544439269738,"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."}}