{"id":"W4282925751","doi":"10.2196/38220","title":"Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute","keywords":"Asthma; Medicine; Emergency department; Cohort; Health care; Emergency medicine; Retrospective cohort study; Medical emergency; Family medicine; Internal medicine; 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":[0.002384763,0.0002153684,0.0005293966,0.000440825,0.0007157421,0.0001463643,0.0008763081,0.00008465662,0.0001255652],"category_scores_gemma":[0.0007946148,0.0002004562,0.00009383845,0.001703429,0.00003592909,0.0003636915,0.001414364,0.0009924789,0.000004893678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002567571,"about_ca_system_score_gemma":0.0001688262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001854779,"about_ca_topic_score_gemma":0.00004052662,"domain_scores_codex":[0.9965231,0.0002441879,0.000781433,0.0003361617,0.001690802,0.000424297],"domain_scores_gemma":[0.9983334,0.0002387819,0.0003233067,0.0004718451,0.0002044902,0.0004281908],"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":[0.00001939773,0.0001743268,0.9639583,0.00006958961,0.0002517336,0.00001289271,0.01979122,0.01101122,2.60283e-7,0.0002833375,0.0001249685,0.004302788],"study_design_scores_gemma":[0.0004644084,0.001178527,0.1798479,0.00001130166,0.00005353443,0.00001542622,0.002455558,0.8145283,4.531329e-7,0.00002269485,0.001233005,0.0001889478],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7278022,0.00002377693,0.2693658,0.0006876045,0.000244998,0.001582006,0.00003132373,0.0002011572,0.00006111022],"genre_scores_gemma":[0.9687869,0.000001538177,0.02994845,0.0006329259,0.00009850432,0.0003854948,0.00005921074,0.00001765117,0.00006935606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8035171,"threshold_uncertainty_score":0.8174368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359156918794111,"score_gpt":0.3239911283997665,"score_spread":0.3103995592118253,"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."}}