{"id":"W4200138766","doi":"10.1109/bibe52308.2021.9635556","title":"Predictive Analytics to Support Health Informatics on COVID-19 Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Predictive analytics; Computer science; Autoencoder; Informatics; Health informatics; Analytics; Health care; Data science; Machine learning; Artificial intelligence; Data analysis; Big data; Data mining; Coronavirus disease 2019 (COVID-19); Disease; Deep learning; Medicine; Infectious disease (medical specialty); Engineering; Pathology","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":[0.001995961,0.0009320017,0.0007132738,0.002295685,0.0005489049,0.001523522,0.00117801,0.0007171763,0.002481951],"category_scores_gemma":[0.009214662,0.0003835921,0.0006679972,0.001815096,0.0003707236,0.001866603,0.002197636,0.00154976,0.00150418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007998279,"about_ca_system_score_gemma":0.000954322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005011668,"about_ca_topic_score_gemma":0.00446842,"domain_scores_codex":[0.9989256,0.0002515541,0.0001128715,0.0003138572,0.0003018223,0.00009415994],"domain_scores_gemma":[0.9961289,0.002093223,0.0003537866,0.000585753,0.0006364165,0.00020202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001171041,0.0008450086,0.04414463,0.0007049092,0.0003646813,0.001987062,0.001106629,0.1260943,0.01846874,0.02093057,0.08775606,0.6964263],"study_design_scores_gemma":[0.00003487388,0.00008632635,0.005550151,0.0001030599,0.00004980193,0.0002934317,0.000202011,0.9422507,0.008267674,0.0259377,0.01718087,0.00004348982],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06611659,0.00137768,0.8427894,0.007062161,0.0005385737,0.0005360439,0.01071746,0.06340786,0.007454273],"genre_scores_gemma":[0.6546652,0.001256622,0.3185842,0.001735259,0.0004359997,0.0002672879,0.01958118,0.0007697492,0.002704465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005011668,"threshold_uncertainty_score":0.0105558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1182582184977587,"score_gpt":0.3765256681276671,"score_spread":0.2582674496299083,"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."}}