{"id":"W3175156399","doi":"10.3390/s21124202","title":"A Machine Learning Approach as an Aid for Early COVID-19 Detection","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Receiver operating characteristic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Learning curve; Computer science; Population; Limit (mathematics); Test (biology); Scale (ratio); Machine learning; Artificial intelligence; Sensitivity (control systems); Order (exchange); Pandemic; Risk analysis (engineering); Medicine; Business; Virology; Engineering; Disease; Geography; Environmental health; Infectious disease (medical specialty); Mathematics; Operating system; Cartography","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":[0.0003359364,0.0001639119,0.0002732612,0.0001449325,0.0002286093,0.0000493697,0.00005712243,0.0001339845,0.0001053998],"category_scores_gemma":[0.002994323,0.000166695,0.000138376,0.0003318551,0.00004639893,0.00006551505,0.00003267936,0.0003062622,0.00004829997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002604533,"about_ca_system_score_gemma":0.0003196798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001009939,"about_ca_topic_score_gemma":0.0001338336,"domain_scores_codex":[0.9985983,0.0001522628,0.0002042209,0.0004976536,0.0002525278,0.0002949778],"domain_scores_gemma":[0.9987721,0.0003128404,0.00007516736,0.0003431476,0.0001395562,0.0003572321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01135799,0.008292402,0.2216217,0.009543307,0.002076332,0.003730264,0.05718773,0.1601106,0.2959694,0.00315158,0.01022344,0.2167353],"study_design_scores_gemma":[0.006686519,0.001742781,0.005301777,0.00008937652,0.0004065832,0.0007164058,0.001301715,0.1215424,0.03883913,0.0004835652,0.8223427,0.0005470607],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840096,0.0001764429,0.004856341,0.009359639,0.0001805404,0.0005144017,0.00001076628,0.0003632811,0.0005290105],"genre_scores_gemma":[0.9774815,0.00003227036,0.004642905,0.013921,0.0003280201,0.00005916592,0.0001163699,0.00006657516,0.003352182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8121192,"threshold_uncertainty_score":0.6797626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04305362004246475,"score_gpt":0.3360731855659155,"score_spread":0.2930195655234507,"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."}}