{"id":"W4226217608","doi":"10.3934/mbe.2022272","title":"A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data","year":2022,"lang":"en","type":"article","venue":"Mathematical Biosciences & Engineering","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; National Research Council Canada; York University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Immune system; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Immunology; Viral infection; Medicine; Virus; Infectious disease (medical specialty); Pathology; Outbreak; Disease","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":[0.001973463,0.0001923124,0.0003496291,0.0004719305,0.0004503168,0.0001138339,0.0001456025,0.00004929014,0.0000268473],"category_scores_gemma":[0.003874293,0.0001769312,0.00003406533,0.0007435127,0.00009712015,0.0001731891,0.0008705982,0.0003467427,0.000002249805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245317,"about_ca_system_score_gemma":0.00008413426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002959929,"about_ca_topic_score_gemma":0.000001252486,"domain_scores_codex":[0.9983015,0.000144476,0.0003287876,0.0005276249,0.0003971068,0.0003004907],"domain_scores_gemma":[0.9984818,0.0007345938,0.00007579395,0.000368875,0.00001766946,0.0003212731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009022643,0.0016851,0.3036101,0.007948347,0.0003891629,0.00006942068,0.01353773,0.2874788,0.3720294,0.003581807,0.00007926168,0.00868871],"study_design_scores_gemma":[0.0007299554,0.0004129227,0.06334213,0.0002117292,0.0002345706,0.0002807326,0.00008388131,0.9262043,0.0006458348,0.0001762008,0.007308905,0.0003688222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9076354,0.000160829,0.09047145,0.00106948,0.0000650727,0.0003863428,0.00001576352,0.0001875194,0.000008148382],"genre_scores_gemma":[0.9941021,0.00001462539,0.00521829,0.0005433622,0.00003288401,0.0000392037,0.00001236476,0.00002524817,0.00001196098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6387255,"threshold_uncertainty_score":0.7215044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08491085373943266,"score_gpt":0.3435286874086572,"score_spread":0.2586178336692245,"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."}}