{"id":"W4318833798","doi":"10.1007/978-3-031-24670-8_20","title":"Deep Learning-Based Multi-modal COVID-19 Screening by Socially Assistive Robots Using Cough and Breathing Symptoms","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences North; Baycrest Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Coronavirus disease 2019 (COVID-19); Transfer of learning; Robot; Architecture; Modal; Machine learning; Human–computer interaction; Medicine; 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":[0.0002932543,0.0008659798,0.0007015978,0.0004374972,0.0002247378,0.0004933701,0.0006745173,0.0008614895,0.002172393],"category_scores_gemma":[0.0008037925,0.0002538041,0.0007004521,0.0002675022,0.0001692911,0.0004247117,0.0008031513,0.0006553102,0.0009946546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000241534,"about_ca_system_score_gemma":0.000349302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003154657,"about_ca_topic_score_gemma":0.005556005,"domain_scores_codex":[0.9997919,0.00004062084,0.00001001684,0.00006681902,0.00004157106,0.00004906224],"domain_scores_gemma":[0.9998128,0.00009054927,0.00001589758,0.00001178116,0.00004699131,0.00002211754],"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":[0.0006534024,0.0004978979,0.01629185,0.0002974006,0.0002079573,0.0007698771,0.0001711603,0.08605567,0.0354249,0.00112255,0.01468677,0.8438205],"study_design_scores_gemma":[0.00002177209,0.0002697254,0.009523155,0.00005683494,0.00009055611,0.0004773037,0.0001044205,0.9751661,0.009794776,0.002038926,0.002420715,0.00003577281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2305674,0.00346888,0.7415644,0.001164347,0.0006056845,0.0002394552,0.001799171,0.004501179,0.0160896],"genre_scores_gemma":[0.8891128,0.000847148,0.09693058,0.0004890889,0.0001685875,0.0001480933,0.001425087,0.0001118022,0.0107668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003154657,"threshold_uncertainty_score":0.007267356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03624407075930807,"score_gpt":0.3221787836988769,"score_spread":0.2859347129395688,"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."}}