{"id":"W4392128090","doi":"10.1016/j.compbiomed.2024.108190","title":"Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks","year":2024,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mel-frequency cepstrum; Computer science; Artificial neural network; Artificial intelligence; Multilayer perceptron; Pattern recognition (psychology); Feature extraction; Respiratory sounds; Speech recognition; Cepstrum; Support vector machine; Perceptron; Machine learning; Asthma","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006745223,0.0001076646,0.0004840691,0.0007523581,0.00003747642,0.000004581249,0.00003561435,0.00009232621,0.000004319596],"category_scores_gemma":[0.00002665807,0.00007614274,0.00005076575,0.0008379833,0.0003523163,0.00002183286,0.00001304933,0.0002116478,5.331924e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001170989,"about_ca_system_score_gemma":0.00001197191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002261611,"about_ca_topic_score_gemma":0.00001344678,"domain_scores_codex":[0.9989305,0.0003404506,0.0002989317,0.0002763889,0.00005649114,0.00009719717],"domain_scores_gemma":[0.99925,0.000480965,0.00005635405,0.0001232731,0.00003654897,0.00005288059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001534864,0.0008980039,0.6428546,0.000217398,0.002224516,0.00006868234,0.00854223,0.001796227,0.01830879,0.00604996,0.0008324878,0.3166723],"study_design_scores_gemma":[0.0009191811,0.003468215,0.6216184,0.0001226583,0.0005886757,0.000003277916,0.001037824,0.3710386,0.0003702278,0.0006549518,0.00009034412,0.00008767068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7890524,0.001111577,0.2088068,0.000362075,0.0001967305,0.0002841938,0.000001941169,0.00004991678,0.0001343329],"genre_scores_gemma":[0.9967762,0.00004594464,0.002683762,0.0003373505,0.0001056106,0.00001759482,0.00002867648,0.000003367498,0.000001497757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3692424,"threshold_uncertainty_score":0.3105011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08963061086171427,"score_gpt":0.4524260629998968,"score_spread":0.3627954521381825,"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."}}