{"id":"W3216598092","doi":"10.21203/rs.3.rs-1062756/v1","title":"Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University","funders":"Warren Y. Soper Charitable Trust; Fondation De Famille Alvin Segal; Jewish General Hospital; Public Health Agency; National Cancer Institute; Ministry of Science and Higher Education of the Russian Federation; Public Health Agency of Canada; Skolkovo Institute of Science and Technology; Genome Canada; McGill University","keywords":"Coronavirus disease 2019 (COVID-19); Omics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computational biology; Computer science; Medicine; Artificial intelligence; Virology; Machine learning; Bioinformatics; Biology; Internal medicine; Outbreak; Disease","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009059929,0.0007803519,0.0008055065,0.0009551721,0.0001860119,0.0009975276,0.0002511902,0.0005574459,0.0007376194],"category_scores_gemma":[0.002061687,0.0001419408,0.0004797657,0.0006150121,0.0002050696,0.0004056631,0.0005204587,0.0007410979,0.0003493303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003466906,"about_ca_system_score_gemma":0.0003406112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000863914,"about_ca_topic_score_gemma":0.0009010874,"domain_scores_codex":[0.99949,0.0001940507,0.00004020481,0.0001327344,0.00007536606,0.00006761339],"domain_scores_gemma":[0.9989605,0.000411696,0.0003389661,0.00007479734,0.0001125563,0.0001015063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001980155,0.0004245102,0.8202405,0.0002059553,0.0004202603,0.0004770089,0.00009591429,0.01861675,0.07873068,0.0005318573,0.00237634,0.07590002],"study_design_scores_gemma":[0.00006771711,0.001626808,0.5604532,0.0001138501,0.0002741027,0.001124562,0.0002465947,0.362879,0.06614788,0.003978552,0.002999599,0.00008798845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9489107,0.002562299,0.04300265,0.0008292785,0.00009379341,0.0000698432,0.003035602,0.0004998674,0.000995886],"genre_scores_gemma":[0.9850689,0.0003479735,0.01284921,0.0001269588,0.00005312835,0.00002086362,0.001289876,0.0000153545,0.0002278715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009975276,"threshold_uncertainty_score":0.004791379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09018038749882826,"score_gpt":0.3808919417793242,"score_spread":0.290711554280496,"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."}}