{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02521166,0.001506655,0.0009854932,0.002882295,0.0005489406,0.002436828,0.001248411,0.001453231,0.001156064],"category_scores_gemma":[0.1013234,0.0003913122,0.000964684,0.001181634,0.0007306809,0.00152341,0.0008289188,0.001564006,0.0005359386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235988,"about_ca_system_score_gemma":0.002287349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00376556,"about_ca_topic_score_gemma":0.002713931,"domain_scores_codex":[0.9922747,0.00450718,0.0008302272,0.0008844527,0.001240238,0.0002631821],"domain_scores_gemma":[0.9324419,0.0521213,0.004701561,0.002639306,0.007600863,0.000495161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007345813,0.0008496859,0.3007928,0.0004705294,0.0008611426,0.000173235,0.0003544299,0.398019,0.002804338,0.0030406,0.003677495,0.2882222],"study_design_scores_gemma":[0.00009121437,0.0007596563,0.02924227,0.0003042039,0.0001651548,0.0001795574,0.0001751494,0.9541871,0.006212137,0.006992138,0.001631016,0.00006052203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4275525,0.002061971,0.559729,0.001791478,0.0002383665,0.001039576,0.001036455,0.002117816,0.004432837],"genre_scores_gemma":[0.8152277,0.0003743932,0.1825782,0.0002603936,0.00007311501,0.0004454506,0.0006222159,0.0000909747,0.0003275356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02521166,"threshold_uncertainty_score":0.1333336,"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."}}