{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009037639,0.0002922078,0.000499312,0.0004065036,0.0001772223,0.000165088,0.0008621865,0.0002334046,0.0000855779],"category_scores_gemma":[0.001273163,0.0002339357,0.00009603983,0.001937617,0.00005634224,0.0008587841,0.0003792872,0.001942158,0.00008475778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154708,"about_ca_system_score_gemma":0.0002269259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002089953,"about_ca_topic_score_gemma":0.00001649881,"domain_scores_codex":[0.9955865,0.0002523558,0.001285565,0.0003614322,0.001999412,0.0005147598],"domain_scores_gemma":[0.9978834,0.0003668362,0.000485272,0.0004469341,0.0001527719,0.0006647983],"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":[0.0005493974,0.0006785927,0.5800923,0.0007823547,0.000401555,0.0001399856,0.1044703,0.0452435,2.171203e-7,0.002511435,0.001312273,0.2638181],"study_design_scores_gemma":[0.001913247,0.001918225,0.263023,0.0001444711,0.000008626403,0.00000181978,0.0002109938,0.7313644,0.000001017414,0.000007678165,0.001217865,0.0001886632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8969933,0.00001279042,0.09196698,0.006071334,0.0002048595,0.0007655559,0.00004251922,0.0008373887,0.003105208],"genre_scores_gemma":[0.9773669,0.000003438636,0.01997218,0.002241231,0.00008224523,0.00003556237,0.0002660321,0.00001820223,0.00001419028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6861209,"threshold_uncertainty_score":0.9539623,"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."}}