{"id":"W3129080628","doi":"10.2196/24572","title":"Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Guangzhou Science, Technology and Innovation Commission; National Health and Medical Research Council; Natural Science Foundation of Guangdong Province","keywords":"Oversampling; Machine learning; Logistic regression; Artificial intelligence; Medicine; Receiver operating characteristic; Predictive power; Medical imaging; Coronavirus disease 2019 (COVID-19); Predictive modelling; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003066771,0.0001404576,0.0005626786,0.0001240548,0.00008857997,0.00002318701,0.0001109809,0.0001020894,0.00001912796],"category_scores_gemma":[0.005035652,0.0001198782,0.0000361714,0.0002288938,0.0001784753,0.0001286139,0.0002676191,0.0003745587,1.695887e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000144954,"about_ca_system_score_gemma":0.001749052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001751901,"about_ca_topic_score_gemma":0.000005250043,"domain_scores_codex":[0.9975404,0.0001568805,0.001078503,0.0002302457,0.0008439436,0.0001500053],"domain_scores_gemma":[0.9979341,0.0007903721,0.0003915145,0.000362239,0.0002186127,0.0003031363],"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":[0.0002090957,0.003117776,0.9740444,0.003834949,0.0002198455,0.0000220581,0.00639445,0.0003365339,0.00001839282,0.00005065372,0.00151356,0.01023832],"study_design_scores_gemma":[0.005002112,0.0004006233,0.1300544,0.0002098537,0.0001131961,0.00001181024,0.002234322,0.8596663,0.0000869513,0.000004008171,0.00211758,0.00009889628],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378098,0.00002623216,0.0575486,0.002716102,0.00006381901,0.00153277,0.0000610097,0.0001431193,0.00009859745],"genre_scores_gemma":[0.8882089,0.00001728809,0.1076642,0.003399493,0.0000245849,0.00005991434,0.0006082757,0.00001213581,0.000005243253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8593298,"threshold_uncertainty_score":0.602851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08671072779037911,"score_gpt":0.4533509668558341,"score_spread":0.366640239065455,"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."}}