{"id":"W4248253778","doi":"10.2196/preprints.25187","title":"Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Population Health Research Institute; Public Health Ontario; University of Toronto; McMaster University; Hamilton Health Sciences","funders":"","keywords":"Vital signs; CINAHL; Medicine; Early warning score; Emergency department; MEDLINE; Machine learning; Acute care; Logistic regression; Artificial intelligence; Medical emergency; Health care; Emergency medicine; Psychological intervention; Computer science; Nursing; Surgery; Internal 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":[0.01257241,0.001656327,0.006990083,0.01009335,0.000725185,0.003659876,0.002513708,0.002455398,0.01219355],"category_scores_gemma":[0.09304239,0.001039691,0.007339629,0.01049425,0.000836938,0.003865944,0.001884342,0.001662006,0.001078139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005301153,"about_ca_system_score_gemma":0.01787192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008724072,"about_ca_topic_score_gemma":0.01939628,"domain_scores_codex":[0.9909399,0.002753218,0.003687649,0.0005213217,0.001847306,0.0002506541],"domain_scores_gemma":[0.9417195,0.04262592,0.009382645,0.0007268574,0.005205417,0.0003396099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001151306,0.00001064164,0.0003168284,0.9668787,0.002483425,0.00003271458,0.0001094316,0.00008765645,0.00005075039,0.0002049446,0.003526942,0.02618293],"study_design_scores_gemma":[0.00009985707,0.00005284683,0.001167724,0.9746161,0.0126968,0.00007408817,0.0001025111,0.00007647761,0.00007552184,0.0001761932,0.01084371,0.0000182427],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008831723,0.9933488,0.0005056427,0.000849447,0.0004311935,0.001585277,0.001632634,0.00003811198,0.0007257859],"genre_scores_gemma":[0.009583101,0.9842418,0.001356827,0.0009465769,0.0002270282,0.002482553,0.0008222847,0.00001658167,0.0003232132],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01257241,"threshold_uncertainty_score":0.06649005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1577106448515891,"score_gpt":0.4284518972989206,"score_spread":0.2707412524473315,"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."}}