{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0152132,0.0004278516,0.0005805707,0.001962675,0.0005959243,0.001427614,0.00136965,0.001030594,0.001837037],"category_scores_gemma":[0.05275547,0.0004844457,0.002094387,0.001946149,0.0003913284,0.001236941,0.0008467294,0.001859546,0.0003647422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008670486,"about_ca_system_score_gemma":0.0009625743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009577059,"about_ca_topic_score_gemma":0.007016156,"domain_scores_codex":[0.9907283,0.003349256,0.001677712,0.001767155,0.001918831,0.0005588357],"domain_scores_gemma":[0.9260836,0.03077313,0.02555173,0.009961459,0.005995964,0.001634235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001569157,0.00002876362,0.9983916,0.00001060847,0.0002000287,0.00002882989,0.0000376012,0.0002006295,0.00001919163,0.00003365292,0.0001988189,0.0006933436],"study_design_scores_gemma":[0.00003059305,0.0003675591,0.9868819,0.00004735119,0.0003800587,0.0004006931,0.0002741861,0.01044211,0.0001362722,0.0001712765,0.000839384,0.00002848889],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930988,0.0006226212,0.002066372,0.0002099026,0.00008065646,0.00006762108,0.003276368,0.00002568139,0.000552048],"genre_scores_gemma":[0.9965051,0.000133545,0.0008347522,0.00005551423,0.00005275838,0.00006517376,0.002147741,0.00001955375,0.0001858491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0152132,"threshold_uncertainty_score":0.08045602,"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."}}