{"id":"W4387456011","doi":"10.2196/44763","title":"Machine Learning Algorithms Predict Successful Weaning From Mechanical Ventilation Before Intubation: Retrospective Analysis From the Medical Information Mart for Intensive Care IV Database","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Human Resource Development; Korea Health Industry Development Institute; Seoul National University; Seoul National University Bundang Hospital","keywords":"Receiver operating characteristic; Mechanical ventilation; Intubation; Random forest; Medicine; Logistic regression; Intensive care unit; Machine learning; Weaning; Intensive care; Artificial intelligence; Emergency medicine; Database; Intensive care medicine; Computer science; Surgery; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003143136,0.0003660156,0.000678183,0.002075575,0.0001970914,0.0008349785,0.0006343307,0.0005467771,0.0005781322],"category_scores_gemma":[0.01123943,0.0002078718,0.0005613572,0.001578852,0.0002019917,0.0005458284,0.0004070928,0.0005784605,0.0002669496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004739532,"about_ca_system_score_gemma":0.0004874692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002802591,"about_ca_topic_score_gemma":0.00215027,"domain_scores_codex":[0.9983638,0.0004554713,0.0003246637,0.0003268754,0.0003966804,0.0001325273],"domain_scores_gemma":[0.9910173,0.004754599,0.001901208,0.0007354722,0.001197062,0.0003942729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002574509,0.00009367793,0.9924254,0.0000235139,0.0001121864,0.00009696271,0.00003047894,0.00184056,0.0001855028,0.00003251411,0.0006614178,0.004240296],"study_design_scores_gemma":[0.00004491869,0.0003069027,0.9455311,0.00003681667,0.0001204461,0.0004695088,0.0001546648,0.05193988,0.000578317,0.00009868037,0.000701544,0.0000172744],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962412,0.0001972924,0.0006219106,0.00006007618,0.000006954926,0.00002969815,0.002667089,0.00001672853,0.0001589681],"genre_scores_gemma":[0.9909321,0.0001261572,0.0007698816,0.00002588567,0.00001294107,0.00003752561,0.008027949,0.000006374735,0.00006105012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003143136,"threshold_uncertainty_score":0.01662266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04181749857274219,"score_gpt":0.3818893606919371,"score_spread":0.3400718621191949,"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."}}