{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02837305,0.001094164,0.0007870578,0.003340926,0.0006253446,0.001370457,0.001473276,0.00100373,0.0009176194],"category_scores_gemma":[0.04335086,0.0005306003,0.001214215,0.001263248,0.0007772822,0.001135296,0.001301455,0.001294696,0.0005548425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232239,"about_ca_system_score_gemma":0.002066443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00497401,"about_ca_topic_score_gemma":0.004343634,"domain_scores_codex":[0.9911944,0.004682431,0.0008674494,0.001409734,0.001520637,0.0003254259],"domain_scores_gemma":[0.9679068,0.01315937,0.003736951,0.004391533,0.009901203,0.0009041986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000644248,0.000660499,0.920557,0.0001514122,0.0004392347,0.000379527,0.0003772753,0.01417848,0.002009346,0.0003003183,0.001416521,0.05888612],"study_design_scores_gemma":[0.0001930656,0.002447116,0.5231634,0.000284889,0.0005828046,0.001398899,0.0009224523,0.4592552,0.006228252,0.001196186,0.004226218,0.0001015139],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9135903,0.0005532544,0.080842,0.0002290643,0.00006304977,0.00148087,0.001972832,0.0002681795,0.001000464],"genre_scores_gemma":[0.9562477,0.0001982886,0.03958208,0.0001046266,0.00004096493,0.0007064398,0.002910875,0.00003492506,0.0001741585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02837305,"threshold_uncertainty_score":0.1500528,"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."}}