{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005846125,0.0002209407,0.0005338514,0.0001949957,0.0001398674,0.00003012107,0.00005851455,0.00008783284,0.0001213647],"category_scores_gemma":[0.007540625,0.0001900496,0.00007111723,0.001009221,0.00007772414,0.00008931323,0.00007726353,0.0002746977,0.000001687534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004313285,"about_ca_system_score_gemma":0.0007348651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003930662,"about_ca_topic_score_gemma":0.0002366812,"domain_scores_codex":[0.9974549,0.0003204948,0.0004882101,0.0006095004,0.0009045693,0.0002223212],"domain_scores_gemma":[0.9973871,0.000706802,0.0002517091,0.0003675134,0.0009295846,0.0003573451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009482853,0.00231592,0.9862057,0.0001733757,0.0002496729,0.0001073172,0.002378294,0.0006566288,0.001509931,0.000009268453,0.005215433,0.0002301494],"study_design_scores_gemma":[0.006340628,0.004038371,0.9705898,0.0001562001,0.0003735806,0.00000752069,0.000743994,0.002583406,0.005022089,0.00001186575,0.009973965,0.0001586495],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536937,0.0001203149,0.0005640578,0.04294537,0.0001018751,0.002184988,0.0002219369,0.0001581671,0.000009563159],"genre_scores_gemma":[0.9924752,0.00001195096,0.0007708021,0.005291049,0.00009975403,0.0006435426,0.0005031506,0.00003162761,0.0001729817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03878141,"threshold_uncertainty_score":0.9027376,"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."}}