{"id":"W2759848381","doi":"10.2196/medinform.8076","title":"A Roadmap for Optimizing Asthma Care Management via Computational Approaches","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute","keywords":"Medicine; Emergency department; Health care; Medical emergency; Identification (biology); Asthma; Disease management; Risk analysis (engineering); Service (business); Intensive care medicine; Operations management; Business; Disease; 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":[0.0004978545,0.0001571348,0.0002041898,0.00009454877,0.00067779,0.0004732466,0.001906542,0.0001340315,0.00001849992],"category_scores_gemma":[0.0001138825,0.0001385948,0.00008619924,0.00006888506,0.0001004484,0.0006419923,0.000781653,0.0003172898,0.0000447128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007345469,"about_ca_system_score_gemma":0.0001174644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001013268,"about_ca_topic_score_gemma":0.000003881758,"domain_scores_codex":[0.9980356,0.00003215519,0.0005105158,0.0001696421,0.0008917394,0.0003603908],"domain_scores_gemma":[0.9983641,0.0001204618,0.0003482471,0.000761849,0.0001178551,0.0002875139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001010305,0.00005182475,0.0006183397,0.00193069,0.00004566771,0.00001487189,0.02015697,0.008704036,4.186295e-8,0.1111904,0.002118599,0.8551584],"study_design_scores_gemma":[0.000637223,0.0000789925,0.001816668,0.0001138711,0.000003255993,0.00002197823,0.000949503,0.976743,0.000001721755,0.001556171,0.01791133,0.0001662282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001979764,0.00003589155,0.9841745,0.003054291,0.0003566198,0.0007073897,0.000003944769,0.0001710911,0.009516456],"genre_scores_gemma":[0.29662,0.000003587885,0.7019088,0.0009885274,0.0001293014,0.0002150696,0.00004023177,0.00001072134,0.00008379056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.968039,"threshold_uncertainty_score":0.5651733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04001762662461163,"score_gpt":0.3348734043327367,"score_spread":0.294855777708125,"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."}}