{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002517315,0.001134644,0.0008571727,0.002823367,0.000415335,0.001417237,0.0008896688,0.001385349,0.002990984],"category_scores_gemma":[0.009042215,0.0001740039,0.0006718785,0.001105373,0.0003139365,0.001029816,0.0006452694,0.001302037,0.001609922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005974917,"about_ca_system_score_gemma":0.0007478687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002729135,"about_ca_topic_score_gemma":0.002663522,"domain_scores_codex":[0.9986356,0.0005996431,0.0001155511,0.0002114845,0.0002838725,0.0001537247],"domain_scores_gemma":[0.9951218,0.003421639,0.000314986,0.0001611079,0.0008079968,0.0001725527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009530579,0.001675814,0.1113179,0.0002944986,0.0003628684,0.0004261452,0.0001517724,0.1204955,0.0129975,0.002926814,0.01131593,0.7370821],"study_design_scores_gemma":[0.00002382404,0.0004598613,0.01099672,0.00005113357,0.00005764843,0.0002958088,0.00009346846,0.9780263,0.004578318,0.003045667,0.002331722,0.00003953611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3648113,0.003974942,0.5945845,0.006976084,0.001036188,0.0005416404,0.001812256,0.005471141,0.02079209],"genre_scores_gemma":[0.8814881,0.0005111111,0.1120797,0.0008663305,0.0002022439,0.0001481321,0.0008274833,0.00003999104,0.003836727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002990984,"threshold_uncertainty_score":0.013313,"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."}}