{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002878344,0.0006394518,0.0003972972,0.001066488,0.0002440159,0.0006474828,0.0006068904,0.0007799578,0.0005385643],"category_scores_gemma":[0.00825232,0.0001932865,0.0007949118,0.000414599,0.0004317823,0.0003937266,0.0005047382,0.0008136333,0.0001693793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006087709,"about_ca_system_score_gemma":0.0006672385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001814078,"about_ca_topic_score_gemma":0.001609445,"domain_scores_codex":[0.9992145,0.0004046648,0.0000759565,0.0001514577,0.0001036459,0.00004974153],"domain_scores_gemma":[0.9954194,0.003280003,0.0004321663,0.0003629001,0.0003749421,0.0001305634],"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":[0.0006788577,0.0009059113,0.08585646,0.0001850283,0.0002988065,0.0003846157,0.0002299369,0.812834,0.01181853,0.003399749,0.00164763,0.08176045],"study_design_scores_gemma":[0.00001166426,0.0001780987,0.004127675,0.000007168841,0.00001281423,0.00008728141,0.00002751108,0.9914759,0.002303937,0.001434581,0.0003227887,0.00001054313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7221796,0.0003663872,0.2716841,0.0007980906,0.0001188193,0.0003742308,0.00211228,0.0009463064,0.001420267],"genre_scores_gemma":[0.9498678,0.00006164124,0.04836834,0.00009402251,0.00002108599,0.0001383373,0.001190156,0.00001280209,0.0002457994],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002878344,"threshold_uncertainty_score":0.01522231,"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."}}