{"id":"W4311163824","doi":"10.18280/ts.390509","title":"Dealing with Imbalanced Sleep Apnea Data Using DCGAN","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discriminator; Computer science; Artificial intelligence; Deep learning; Sleep apnea; Polysomnography; Classifier (UML); Apnea; Pattern recognition (psychology); Test data; Machine learning; Medicine; Cardiology","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":[0.0007130176,0.001033234,0.0004769473,0.0006509213,0.0002563569,0.0005030186,0.000494628,0.0005015042,0.0009056143],"category_scores_gemma":[0.001960112,0.0002227213,0.0005538331,0.0004660031,0.0002870935,0.001086198,0.0007861434,0.0008356403,0.0004554791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000425875,"about_ca_system_score_gemma":0.0003508636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002829913,"about_ca_topic_score_gemma":0.004684725,"domain_scores_codex":[0.9997187,0.00005921911,0.00001981227,0.0000830553,0.00006550095,0.0000536472],"domain_scores_gemma":[0.9993854,0.0002753991,0.0000542413,0.0001298809,0.000133363,0.00002185786],"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":[0.00098052,0.0005023018,0.02060497,0.0003945949,0.0003169141,0.0006712928,0.000247765,0.2076317,0.06132223,0.002020955,0.01569768,0.6896091],"study_design_scores_gemma":[0.00003784034,0.0002660881,0.01170086,0.00006081029,0.0000917976,0.0003131737,0.0001474237,0.9455672,0.03192887,0.00301258,0.006836737,0.00003663601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6122091,0.003354174,0.3606358,0.002196357,0.001327944,0.0003241122,0.004483936,0.0055044,0.009964203],"genre_scores_gemma":[0.918908,0.000697819,0.06874886,0.000587293,0.0001495093,0.0001506851,0.007314419,0.0001170895,0.00332635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002829913,"threshold_uncertainty_score":0.005626857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06073331592409083,"score_gpt":0.3182663985039278,"score_spread":0.257533082579837,"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."}}