{"id":"W3001400935","doi":"10.2196/16080","title":"Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Intermountain Healthcare","keywords":"Asthma; Medicine; Medical emergency; Intensive care medicine; Pediatrics; Internal medicine","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":[0.005276439,0.001104496,0.001267168,0.002786707,0.0005850699,0.001315225,0.001363395,0.0008490919,0.002251858],"category_scores_gemma":[0.01086178,0.0003707791,0.003080393,0.001291265,0.0002910283,0.0006926219,0.0009310201,0.001626954,0.0006373628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001541572,"about_ca_system_score_gemma":0.002077632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02036977,"about_ca_topic_score_gemma":0.01055752,"domain_scores_codex":[0.9984367,0.0007014981,0.0001315293,0.0003373698,0.0002096501,0.0001832952],"domain_scores_gemma":[0.9929986,0.00443217,0.0007101064,0.0003434345,0.001229602,0.0002861414],"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":[0.0008320601,0.001438141,0.8380604,0.000180462,0.001062329,0.0003731014,0.0001832981,0.1185527,0.0007876903,0.0006453183,0.006953839,0.03093057],"study_design_scores_gemma":[0.0001072951,0.0005360226,0.09079627,0.00005091168,0.0003486727,0.0001808035,0.0001761908,0.9049706,0.000584539,0.001028811,0.001186901,0.00003303255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9609448,0.0002411866,0.0278258,0.001122586,0.00008531101,0.0005459626,0.007617394,0.0003422821,0.001274631],"genre_scores_gemma":[0.9766917,0.00009898305,0.0117832,0.0001594357,0.00007581683,0.000401609,0.01018984,0.00002174294,0.0005776442],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02036977,"threshold_uncertainty_score":0.04050243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617513048227109,"score_gpt":0.2989055907700519,"score_spread":0.2827304602877808,"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."}}