{"id":"W4282590599","doi":"10.3390/s22124341","title":"Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Machine learning; Respiratory rate; Artificial intelligence; Computer science; Algorithm; Decision tree; Coronavirus disease 2019 (COVID-19); Oxygen saturation; Support vector machine; Inference; Heart rate; Medicine; Infectious disease (medical specialty); Internal 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":[0.001187613,0.001213202,0.0007429955,0.001183253,0.0001896805,0.001015798,0.0008951852,0.001421678,0.002713308],"category_scores_gemma":[0.00358526,0.0002521408,0.0007312846,0.0005945023,0.000191678,0.0009972223,0.0008438061,0.001023449,0.001873065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003658496,"about_ca_system_score_gemma":0.0004390363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149343,"about_ca_topic_score_gemma":0.001412007,"domain_scores_codex":[0.9993082,0.0002020381,0.00005610327,0.0001959046,0.0001772503,0.00006050742],"domain_scores_gemma":[0.999056,0.0003952767,0.00007728459,0.00006402231,0.0003564651,0.00005091507],"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":[0.0005303022,0.0003149596,0.01750343,0.0005571917,0.0001811227,0.0005375894,0.0001587839,0.02951724,0.04178601,0.002208501,0.009898202,0.8968066],"study_design_scores_gemma":[0.00006475065,0.000718467,0.01223571,0.000285361,0.0001626487,0.001170472,0.0002213405,0.9158447,0.04377333,0.005776611,0.01966684,0.00007978088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06105591,0.003862155,0.9234854,0.001521984,0.000342458,0.0002641275,0.0007697586,0.004808789,0.003889464],"genre_scores_gemma":[0.4471517,0.00236374,0.5413008,0.001137064,0.0003165717,0.0003714273,0.001861641,0.0001335199,0.005363662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002713308,"threshold_uncertainty_score":0.009076893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06665898170698173,"score_gpt":0.3644744155449904,"score_spread":0.2978154338380087,"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."}}