{"id":"W3207263822","doi":"10.2196/29392","title":"Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Triage; Univariate; Machine learning; Artificial intelligence; Dimensionality reduction; Observational study; Sample size determination; Medicine; Computer science; Receiver operating characteristic; Feature selection; Classifier (UML); Confidence interval; Data mining; Statistics; Emergency medicine; Internal medicine; Mathematics; Multivariate statistics","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":[0.004142945,0.0004910413,0.0007822556,0.0009650917,0.0004652665,0.0009253395,0.0008577899,0.0007274727,0.0008529603],"category_scores_gemma":[0.01116666,0.0004671448,0.0008335706,0.0011621,0.000523722,0.000754681,0.0007239187,0.001407956,0.0003998526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005988748,"about_ca_system_score_gemma":0.0009012982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004014179,"about_ca_topic_score_gemma":0.002616064,"domain_scores_codex":[0.9976547,0.0009004152,0.0002535256,0.0004764998,0.0004736258,0.0002412143],"domain_scores_gemma":[0.9903798,0.003549814,0.002588919,0.001545384,0.001244405,0.0006918103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003596932,0.0001692308,0.996868,0.00001782713,0.0001076001,0.0001487784,0.00006208451,0.0003990062,0.0001231004,0.00003181208,0.0003871559,0.001325806],"study_design_scores_gemma":[0.00006409974,0.001050071,0.9866997,0.00003532716,0.000112402,0.0008758041,0.0003530245,0.00980208,0.0001675009,0.0001181739,0.0006989437,0.00002293999],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980687,0.0001376145,0.0008123802,0.00004093211,0.000009836633,0.00004243333,0.0006958083,0.000006598733,0.0001856093],"genre_scores_gemma":[0.997766,0.00008550278,0.0005215261,0.0000343547,0.00001902211,0.00004266095,0.001458282,0.000005225237,0.00006749804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004142945,"threshold_uncertainty_score":0.02191025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1289569268690264,"score_gpt":0.3653010516768117,"score_spread":0.2363441248077852,"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."}}