{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001879781,0.000344791,0.0005608632,0.001168278,0.000555005,0.0001078704,0.0007614005,0.00009098763,0.0009745177],"category_scores_gemma":[0.0006279952,0.0002932846,0.00007322599,0.0006210924,0.0002211715,0.0002086989,0.000620253,0.001059513,0.0000690932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008594688,"about_ca_system_score_gemma":0.001156009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003517153,"about_ca_topic_score_gemma":0.00004184686,"domain_scores_codex":[0.9962341,0.0000961512,0.001230789,0.0003563178,0.001649715,0.0004329575],"domain_scores_gemma":[0.9973829,0.0004316355,0.0007456874,0.0007058486,0.0001570113,0.0005768531],"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":[0.001991592,0.001568214,0.02672154,0.002284947,0.000876529,0.0001592001,0.01637225,0.001911299,0.001260204,0.005321676,0.364671,0.5768616],"study_design_scores_gemma":[0.003659808,0.003084913,0.0008014296,0.0003309338,0.00006185246,0.0005451501,0.004483876,0.3723531,0.00002235742,0.0001983111,0.6140859,0.0003723147],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1792289,0.0006532635,0.05783906,0.7195249,0.01219862,0.004994852,0.004347586,0.001390939,0.01982184],"genre_scores_gemma":[0.7925802,0.002885075,0.001582033,0.1957116,0.0005655399,0.00009133176,0.005364344,0.0000462332,0.001173582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6133513,"threshold_uncertainty_score":0.999952,"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."}}