{"id":"W3082810340","doi":"10.1038/s41467-020-20816-7","title":"Real-time prediction of COVID-19 related mortality using electronic health records","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Institute of Biomedical Imaging and Bioengineering; Deutsche Forschungsgemeinschaft; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Cohort; Confidence interval; Disease; Cohort study; Transmission (telecommunications); Risk assessment; Relative risk; Risk of mortality; Health care","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":[0.002709753,0.0005102091,0.0005443899,0.002273171,0.0002284087,0.001203085,0.0005666972,0.0007500298,0.001172929],"category_scores_gemma":[0.01593843,0.0002122083,0.0006893858,0.001721944,0.0001349789,0.0008597185,0.0006862395,0.0008437081,0.0006716849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005066929,"about_ca_system_score_gemma":0.0005528777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01068789,"about_ca_topic_score_gemma":0.009998479,"domain_scores_codex":[0.9982283,0.0006479653,0.0002925841,0.0004012148,0.0002972237,0.0001326636],"domain_scores_gemma":[0.9906138,0.005081281,0.002227384,0.0006192192,0.0010485,0.0004097714],"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":[0.0003692938,0.000154985,0.9726374,0.00006405563,0.0001708005,0.00007629527,0.00004452964,0.004178456,0.0002364794,0.0001280335,0.001935945,0.0200037],"study_design_scores_gemma":[0.00008890905,0.0004457245,0.8589826,0.0001001724,0.0001523183,0.0002760502,0.0002516128,0.1360546,0.001039583,0.0005321226,0.002032298,0.00004392511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707007,0.000627335,0.005666262,0.0007774762,0.00008432144,0.00009655219,0.02064483,0.0002225764,0.001179951],"genre_scores_gemma":[0.973396,0.0003776498,0.005920622,0.0001161774,0.00009714418,0.00007226148,0.01972057,0.000009807978,0.0002897848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01068789,"threshold_uncertainty_score":0.02125138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04959051882208852,"score_gpt":0.3951500276849158,"score_spread":0.3455595088628272,"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."}}