{"id":"W4391925924","doi":"10.21203/rs.3.rs-3912968/v1","title":"Demystifying COVID-19 Mortality Causes with Interpretable Data Mining","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Shanghai Municipal Health Commission","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Data mining; Computer science; Data science; Geography; Virology; Medicine; Internal medicine; Outbreak; Infectious disease (medical specialty)","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":[0.003053205,0.001161778,0.001020333,0.007121244,0.0004852571,0.002049287,0.001301142,0.0008572993,0.001070535],"category_scores_gemma":[0.007784159,0.0002464313,0.00156219,0.003506626,0.0002600173,0.0008523197,0.001303894,0.001056123,0.0004845007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006423111,"about_ca_system_score_gemma":0.001015813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005198524,"about_ca_topic_score_gemma":0.005664663,"domain_scores_codex":[0.9986998,0.0002626134,0.0003062962,0.000277047,0.0003032054,0.0001509838],"domain_scores_gemma":[0.9959568,0.001738152,0.0008185538,0.0003800924,0.0009352333,0.0001711753],"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":[0.0007031672,0.0005310382,0.7050425,0.0009191938,0.00065183,0.003839859,0.0004935074,0.03329786,0.00525588,0.002366351,0.02268485,0.224214],"study_design_scores_gemma":[0.0001021297,0.0003217772,0.2532179,0.0005090379,0.0006030823,0.001954555,0.001378832,0.6993777,0.0102836,0.01048469,0.02164346,0.0001231712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7305173,0.006370001,0.186481,0.005646038,0.0009863011,0.001018771,0.05810266,0.005736094,0.00514187],"genre_scores_gemma":[0.8494474,0.001459052,0.0975147,0.0004376562,0.0003568862,0.0003594459,0.04944351,0.000106334,0.0008750699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007121244,"threshold_uncertainty_score":0.01614708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.370068581574596,"score_gpt":0.5434794758159253,"score_spread":0.1734108942413292,"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."}}