{"id":"W4206792129","doi":"10.1109/bibm52615.2021.9669569","title":"Health Analytics on Big COVID-19 Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Autoencoder; Computer science; Machine learning; Artificial intelligence; Identification (biology); Analytics; Predictive analytics; Big data; Test data; Coronavirus disease 2019 (COVID-19); Data mining; Data analysis; Predictive modelling; Deep learning; Data science; Disease; Infectious disease (medical specialty); Medicine","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":[0.0006835295,0.0002972875,0.0005544976,0.0006597731,0.0001518194,0.0001524681,0.0004342821,0.0001276956,0.0008077016],"category_scores_gemma":[0.001166014,0.000239083,0.00007346247,0.0006014104,0.0002277463,0.0001395231,0.0002477019,0.000387512,0.0001315705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003632363,"about_ca_system_score_gemma":0.00224648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002432052,"about_ca_topic_score_gemma":0.0001276535,"domain_scores_codex":[0.9972544,0.00004796492,0.0008016826,0.000496599,0.00105399,0.000345373],"domain_scores_gemma":[0.9972375,0.0003646743,0.0003313125,0.0009446997,0.000434284,0.0006875037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005833405,0.001690642,0.003108754,0.001641918,0.0009158871,0.0006687891,0.001974337,0.00008227069,0.001277507,0.01780121,0.6861202,0.2841352],"study_design_scores_gemma":[0.00568079,0.002057287,0.002208217,0.002150933,0.0001702559,0.0003816392,0.00186141,0.2401887,0.0007350519,0.0006241701,0.7433877,0.0005538182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0102267,0.0003175822,0.01591009,0.9579493,0.003312725,0.0006419489,0.001487974,0.0001267771,0.01002695],"genre_scores_gemma":[0.4943202,0.01164948,0.007997664,0.4725094,0.002085832,0.00002503036,0.007373874,0.0000594017,0.003979124],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.4854398,"threshold_uncertainty_score":0.974952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.244183654479877,"score_gpt":0.4297195157071462,"score_spread":0.1855358612272692,"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."}}