{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309461,0.0001162113,0.0002407219,0.0001239748,0.0005128802,0.00003577468,0.001647471,0.0002684281,0.0000352395],"category_scores_gemma":[0.0009111581,0.0001305185,0.00008261135,0.001330477,0.00008082444,0.0002040885,0.0006668436,0.001627194,0.000005441912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006653233,"about_ca_system_score_gemma":0.003446909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001825004,"about_ca_topic_score_gemma":0.001104266,"domain_scores_codex":[0.9970653,0.001468555,0.0005325354,0.0003393074,0.0002936147,0.000300675],"domain_scores_gemma":[0.994957,0.000449487,0.000410922,0.003741981,0.0002547804,0.0001858635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001192994,0.0006121449,0.1447068,0.0004255775,0.0002497967,0.000008717531,0.00634309,0.004555678,0.004822988,0.8241852,0.006916023,0.007162054],"study_design_scores_gemma":[0.0007015964,0.0002232289,0.100782,0.0001665481,0.00004677137,0.0002003618,0.0001610379,0.8234794,0.0002174956,0.02120596,0.0524439,0.0003716877],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3851576,0.07620253,0.1419466,0.3548776,0.003017889,0.003325947,0.0005440754,0.004735181,0.03019258],"genre_scores_gemma":[0.9261771,0.002703855,0.0697138,0.00087481,0.0000252891,0.00001364933,0.0002666129,0.00001614343,0.0002087421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8189238,"threshold_uncertainty_score":0.706944,"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."}}