{"id":"W4379983370","doi":"10.1109/icit58465.2023.10143145","title":"Mining Big Healthcare Data to Predict Long COVID Cases","year":2023,"lang":"en","type":"article","venue":"","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Big data; AKA; Data science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Business; Analytics; Health care; Computer science; Data mining; Disease; Medicine; Economic growth; Economics; Virology; Infectious disease (medical specialty)","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":[0.001869491,0.001446167,0.00149192,0.004445813,0.0006991973,0.001805105,0.001425983,0.001798347,0.001134395],"category_scores_gemma":[0.01005002,0.0005336697,0.001762518,0.003013155,0.0003584438,0.00156633,0.001637313,0.001446269,0.0007952683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006450511,"about_ca_system_score_gemma":0.001452197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006110064,"about_ca_topic_score_gemma":0.01135242,"domain_scores_codex":[0.9978878,0.000469141,0.0004501629,0.0005834295,0.0003891612,0.0002204301],"domain_scores_gemma":[0.9932945,0.003292273,0.001164508,0.0007822886,0.0007873902,0.0006788349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0014239,0.001750124,0.6609219,0.001359547,0.001291574,0.004260966,0.0005683536,0.05976531,0.004897767,0.002451146,0.04354984,0.2177595],"study_design_scores_gemma":[0.000240567,0.0007846534,0.1655428,0.0005319084,0.0005676929,0.002433304,0.002190077,0.7803851,0.006502935,0.02204759,0.01864815,0.000125247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7158657,0.008317455,0.1370991,0.02127584,0.001196762,0.001433289,0.1044997,0.003931755,0.006380411],"genre_scores_gemma":[0.8452146,0.002125727,0.07509556,0.001648432,0.0006373543,0.0005143853,0.07357062,0.00007388709,0.001119373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006110064,"threshold_uncertainty_score":0.01214904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1295783362341015,"score_gpt":0.4039041846838676,"score_spread":0.2743258484497662,"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."}}