{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002836197,0.0005033893,0.000365277,0.0009453786,0.0005475163,0.0007649215,0.000919188,0.000551469,0.001653691],"category_scores_gemma":[0.00985327,0.0004128912,0.000571858,0.0009602522,0.000318139,0.0007468755,0.0006479807,0.0009768988,0.0004708854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559187,"about_ca_system_score_gemma":0.001230582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04086009,"about_ca_topic_score_gemma":0.0575759,"domain_scores_codex":[0.9988887,0.0003127004,0.0001166292,0.0002758774,0.0002652174,0.0001409982],"domain_scores_gemma":[0.9962476,0.0009099775,0.0008524237,0.0006622071,0.001003217,0.0003245132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003825914,0.0001778579,0.994698,0.00001598222,0.0001109042,0.00005881981,0.00006857223,0.0003022246,0.0001357096,0.00004233433,0.0004296988,0.00357727],"study_design_scores_gemma":[0.00005733126,0.0003881779,0.9818037,0.00002941287,0.000181126,0.0003512726,0.0003617531,0.01589901,0.0002407871,0.0001821067,0.0004780761,0.00002717756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973768,0.00009220971,0.001001435,0.00005913038,0.00001045662,0.00005152949,0.00119502,0.00001310411,0.0002003325],"genre_scores_gemma":[0.9967217,0.00008394045,0.0009960673,0.00003093686,0.00001118042,0.00006780657,0.001772531,0.000007241154,0.0003087265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04086009,"threshold_uncertainty_score":0.08124453,"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."}}