{"id":"W2521626508","doi":"10.11159/icbes16.117","title":"A Time-Series Approach to Predict Obstructive Sleep Apnea (OSA) Episodes","year":2016,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Obstructive sleep apnea; Series (stratigraphy); Sleep (system call); Computer science; Medicine; Time series; Sleep apnea; Internal medicine; Machine learning","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":[0.0005573467,0.0007859895,0.0005740742,0.001689722,0.0003275415,0.0007290707,0.0005179415,0.0007666368,0.001711146],"category_scores_gemma":[0.001618453,0.0001863174,0.0006439074,0.0008716629,0.0001170791,0.0005728916,0.0002606291,0.0007133014,0.0004775044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003352951,"about_ca_system_score_gemma":0.0005280904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007027953,"about_ca_topic_score_gemma":0.004533015,"domain_scores_codex":[0.9997292,0.0000449972,0.00003661209,0.0000840547,0.00007231609,0.00003283944],"domain_scores_gemma":[0.999503,0.0002532689,0.00005667991,0.00001817262,0.0001409536,0.00002789317],"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":[0.0004895784,0.0008193548,0.03729949,0.0002186531,0.0003246075,0.0005271255,0.0001631866,0.3686631,0.01042111,0.00313043,0.004643016,0.5733004],"study_design_scores_gemma":[0.00000404656,0.00007575966,0.003253132,0.00001100284,0.00002851824,0.00004853059,0.00002951501,0.9947962,0.0006151936,0.0005740461,0.0005555208,0.000008430322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2525098,0.001935581,0.7351046,0.0008456987,0.0003821439,0.0002928242,0.001541514,0.002306724,0.00508117],"genre_scores_gemma":[0.9101791,0.0007683426,0.08449791,0.000105865,0.0001641829,0.000196429,0.001251169,0.00003887517,0.00279811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007027953,"threshold_uncertainty_score":0.01397407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007884103529609433,"score_gpt":0.2168509083849259,"score_spread":0.2089668048553165,"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."}}