{"id":"W4214527673","doi":"10.2196/33043","title":"Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health; Flight Attendant Medical Research Institute; National Heart, Lung, and Blood Institute; Tobacco-Related Disease Research Program","keywords":"COPD; Medicine; Generalizability theory; Exacerbation; Emergency department; Psychological intervention; Cohort; Retrospective cohort study; Pulmonary disease; Intensive care medicine; Obstructive lung disease; Cohort study; Emergency medicine; Machine learning; Artificial intelligence; Internal medicine; Computer science","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":[0.006134942,0.0006381001,0.0007323299,0.001237891,0.0007834327,0.001136113,0.001114976,0.001123748,0.001256397],"category_scores_gemma":[0.03039494,0.0007723236,0.001294201,0.0009501215,0.000473757,0.0008579563,0.001081311,0.001761225,0.0004634024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006222913,"about_ca_system_score_gemma":0.000821371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01191937,"about_ca_topic_score_gemma":0.01238852,"domain_scores_codex":[0.9972551,0.0008832021,0.0003510508,0.0008144059,0.0004622505,0.0002340158],"domain_scores_gemma":[0.9730577,0.01202292,0.005581483,0.00588886,0.00271467,0.0007343052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003040918,0.0002284246,0.9960309,0.00002105347,0.0001811463,0.000169163,0.0002089177,0.0002842422,0.0001429575,0.00003000914,0.0005063749,0.001892595],"study_design_scores_gemma":[0.00008190011,0.0004785132,0.9888608,0.00004243924,0.000258104,0.0008291742,0.000450624,0.007588875,0.0002810594,0.0001501496,0.0009332494,0.00004496482],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974973,0.0001016176,0.0004533079,0.00004070906,0.00001058968,0.00006736278,0.001680032,0.00001249949,0.0001364783],"genre_scores_gemma":[0.9944381,0.0001064762,0.000804158,0.00004374752,0.00002189264,0.00007900332,0.004366004,0.00001391834,0.0001267106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01191937,"threshold_uncertainty_score":0.03244507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285520481416921,"score_gpt":0.2971062855833635,"score_spread":0.2842510807691943,"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."}}