{"id":"W2951144698","doi":"10.1097/mlr.0000000000001140","title":"Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits","year":2019,"lang":"en","type":"article","venue":"Medical Care","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; National Center for Advancing Translational Sciences; Agency for Healthcare Research and Quality","keywords":"Machine learning; Emergency department; Artificial intelligence; Random forest; AdaBoost; Referral; Number needed to treat; Medicine; Receiver operating characteristic; Algorithm; Context (archaeology); Computer science; Confidence interval; Physical therapy; Relative risk; Support vector machine; Family medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001280672,0.0001587824,0.0002730336,0.0000601501,0.0002772541,0.000006851136,0.0001245129,0.0002007115,0.001092626],"category_scores_gemma":[0.0005739614,0.0001305928,0.00006701296,0.0001055671,0.00001396807,0.00006071176,0.0002256732,0.0009454727,0.000480031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008630472,"about_ca_system_score_gemma":0.00008729459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000191314,"about_ca_topic_score_gemma":0.0008395315,"domain_scores_codex":[0.9974927,0.0006962395,0.0004403392,0.0003698243,0.0005341453,0.0004668029],"domain_scores_gemma":[0.9990973,0.0001428797,0.0001250015,0.0001627484,0.0001042079,0.0003678835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007350903,0.00003728962,0.68714,0.0003056873,0.00003581344,0.00001558824,0.03986755,0.000001453814,0.0000945475,0.00001390793,0.0004144932,0.2720001],"study_design_scores_gemma":[0.002126999,0.0005187703,0.9047405,0.001503174,0.00008879012,0.000004604003,0.0151825,0.01382385,0.000002026106,0.0001485206,0.06145497,0.0004053058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909452,0.001998881,0.0009154389,0.0004177771,0.001275439,0.0006512763,0.00004084389,0.00009123758,0.003663934],"genre_scores_gemma":[0.9968534,0.0003179281,0.0005230758,0.0007171755,0.0002963579,0.0001554303,0.00008555817,0.00002751231,0.001023632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2715948,"threshold_uncertainty_score":0.9998205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02069541479767775,"score_gpt":0.3728159846891945,"score_spread":0.3521205698915167,"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."}}