{"id":"W3116632780","doi":"10.2196/21965","title":"Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Interpretability; Asthma; Psychological intervention; Medicine; Machine learning; Health care; Artificial intelligence; Predictive modelling; Cohort; Computer science; Nursing","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":[0.008896378,0.001194644,0.0009216167,0.002914147,0.0004967457,0.001441784,0.001322115,0.001117914,0.002552774],"category_scores_gemma":[0.05067774,0.0004303729,0.002512405,0.001705162,0.0004182977,0.0008748719,0.001313335,0.001970841,0.0007062383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361484,"about_ca_system_score_gemma":0.002059643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01019903,"about_ca_topic_score_gemma":0.01212373,"domain_scores_codex":[0.995161,0.002590517,0.000500641,0.0009894785,0.0005324276,0.0002259143],"domain_scores_gemma":[0.9236032,0.06241027,0.004702137,0.004818539,0.003914812,0.0005510076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001206338,0.001251685,0.8256626,0.0006704783,0.0008698933,0.0007209263,0.0009794083,0.04652453,0.001581801,0.00136555,0.02338156,0.09578529],"study_design_scores_gemma":[0.000492303,0.0008442082,0.2909808,0.0005097,0.0009671734,0.000563257,0.00104318,0.6728154,0.006029353,0.01007329,0.01550333,0.0001779636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8320107,0.001100398,0.09323157,0.005070178,0.0003789081,0.001301418,0.05927042,0.003572367,0.004064003],"genre_scores_gemma":[0.9041181,0.0001922092,0.05633081,0.0004766041,0.0002008136,0.0005607701,0.03746945,0.000116038,0.0005351975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01019903,"threshold_uncertainty_score":0.04704911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006633030092116837,"score_gpt":0.2467374454739288,"score_spread":0.240104415381812,"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."}}