{"id":"W4360989109","doi":"10.18280/ria.370125","title":"Detection and Classification of Obstructive Sleep Apnea Disorders: A Comparative Analysis of Various Deep Machine Learning Classifiers","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Obstructive sleep apnea; Artificial intelligence; Computer science; Sleep (system call); Machine learning; Physical medicine and rehabilitation; Psychology; Medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002345393,0.0007634287,0.0009079609,0.002856906,0.0002551356,0.001054311,0.0005876292,0.0008422782,0.001012699],"category_scores_gemma":[0.004406091,0.0001422576,0.0008577617,0.001447621,0.0001651445,0.001024085,0.0004338161,0.0006587326,0.0003940195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005208798,"about_ca_system_score_gemma":0.0007189425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005153087,"about_ca_topic_score_gemma":0.003946462,"domain_scores_codex":[0.9988789,0.0002801777,0.000156045,0.0001668472,0.0004057498,0.0001124325],"domain_scores_gemma":[0.9976543,0.001560756,0.00009942443,0.0000647051,0.0005477059,0.00007319487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008660425,0.0004564478,0.04121137,0.000871193,0.000525651,0.0001245191,0.0001323088,0.04589842,0.002281928,0.001047012,0.007436037,0.8991491],"study_design_scores_gemma":[0.0000931148,0.001263589,0.04734463,0.0006021811,0.0007521824,0.0003159936,0.0003999788,0.9328922,0.004388571,0.003246287,0.008631874,0.00006943523],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6776408,0.101311,0.1939926,0.002718035,0.001043074,0.0003562431,0.003439875,0.001804219,0.01769423],"genre_scores_gemma":[0.9353245,0.01602785,0.04155278,0.0003306039,0.0002229508,0.0001092246,0.003239147,0.00006745999,0.003125276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005153087,"threshold_uncertainty_score":0.01240379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05332319748075792,"score_gpt":0.3268314977168874,"score_spread":0.2735083002361294,"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."}}