{"id":"W4226059105","doi":"10.1016/j.compbiomed.2022.105467","title":"COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; 2019-20 coronavirus outbreak; Artificial intelligence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Machine learning; Algorithm; Data mining; Medicine; Virology; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002955163,0.000963115,0.001027516,0.001572735,0.0003370129,0.00094422,0.001154807,0.0007044167,0.0008655434],"category_scores_gemma":[0.004216789,0.0002574477,0.001116014,0.001189622,0.0004004608,0.0004484834,0.000983352,0.0006972987,0.000406697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007848828,"about_ca_system_score_gemma":0.0006423193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003705526,"about_ca_topic_score_gemma":0.003427116,"domain_scores_codex":[0.999036,0.0003373473,0.00007669404,0.0002862238,0.0001616871,0.0001020819],"domain_scores_gemma":[0.9980068,0.000829696,0.0002681671,0.0004540406,0.0002690217,0.000172303],"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":[0.004033619,0.0008953966,0.8227621,0.0003787279,0.001543511,0.0009890724,0.0002103065,0.06939536,0.006404359,0.0002649331,0.009879208,0.08324354],"study_design_scores_gemma":[0.000426129,0.001724129,0.695924,0.00009587167,0.0006806921,0.002832525,0.0005668463,0.2793342,0.009907045,0.0008195429,0.007559555,0.0001295807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857876,0.0004540336,0.004077886,0.000183273,0.00002283212,0.000081341,0.008779087,0.0002336067,0.0003803844],"genre_scores_gemma":[0.9608287,0.0001588544,0.005322085,0.00005663409,0.0000352592,0.0001017985,0.03324644,0.00003643834,0.0002138272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003705526,"threshold_uncertainty_score":0.01562864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03574601820081173,"score_gpt":0.354767890532422,"score_spread":0.3190218723316103,"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."}}