{"id":"W4313527334","doi":"10.1109/bibm55620.2022.9995592","title":"Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Big data; Pandemic; Informatics; Computer science; Health informatics; Data science; Health care; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Data mining; Medicine; Engineering; Disease; Infectious disease (medical specialty); Political science","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":[0.002232687,0.0008416148,0.001264943,0.001575454,0.0007878705,0.001137078,0.001662901,0.001737876,0.001436485],"category_scores_gemma":[0.008278309,0.0006158486,0.001085649,0.0009774799,0.00106765,0.001767422,0.001545669,0.001930408,0.0003176423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179451,"about_ca_system_score_gemma":0.001443809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02517073,"about_ca_topic_score_gemma":0.01870423,"domain_scores_codex":[0.9992356,0.0002885863,0.0000420753,0.0002340047,0.0001168189,0.00008292576],"domain_scores_gemma":[0.9959526,0.003123514,0.0001997647,0.0002535792,0.0002991417,0.0001713003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003067075,0.000346629,0.009991174,0.0002006758,0.0001321958,0.0004299237,0.0003438792,0.8658925,0.001233562,0.01202465,0.006650763,0.1024473],"study_design_scores_gemma":[0.00000575687,0.00001532656,0.0003676838,0.000007442735,0.000005086969,0.00001670661,0.00001987853,0.9911835,0.0001777511,0.007960062,0.0002352632,0.0000055218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1842025,0.001728219,0.7999663,0.005242438,0.0002240849,0.0002981798,0.001732347,0.002303123,0.004302877],"genre_scores_gemma":[0.8915033,0.0005973263,0.1007838,0.001011969,0.0002067228,0.0001964668,0.003028428,0.0000665013,0.002605546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02517073,"threshold_uncertainty_score":0.05004841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2455717761894158,"score_gpt":0.4181600665094102,"score_spread":0.1725882903199944,"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."}}