{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003523111,0.0001654023,0.0003101596,0.0003472054,0.0001346444,0.0001033767,0.0003105248,0.00003554937,6.476624e-7],"category_scores_gemma":[0.000174163,0.00008976511,0.00003841101,0.001011532,0.000402291,0.0002046716,0.0002190999,0.0001573651,0.000001332299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007134485,"about_ca_system_score_gemma":0.00001788333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004201221,"about_ca_topic_score_gemma":7.43677e-8,"domain_scores_codex":[0.9984262,0.000006141793,0.000204212,0.0004370936,0.0005432041,0.0003831439],"domain_scores_gemma":[0.9991735,0.00009409626,0.00006574467,0.0001275878,0.0002822272,0.0002568568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001417058,0.0005327159,0.04664256,0.001936375,0.0005212743,0.00001046064,0.001200348,0.001347909,0.4377041,0.2875729,0.00137122,0.2197431],"study_design_scores_gemma":[0.001389992,0.00119173,0.06199629,0.0008862918,0.00005372223,0.0003723861,0.00005238845,0.8867649,0.04540832,0.00008431719,0.001348639,0.0004509551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923425,0.0002230158,0.003503159,0.0008047147,0.000594395,0.0008372717,0.000005687848,0.0001047777,0.001584463],"genre_scores_gemma":[0.99717,0.000004294864,0.001787181,0.00002197767,0.0001521754,0.0000374597,6.609854e-8,0.00001266684,0.0008142146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.885417,"threshold_uncertainty_score":0.3660515,"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."}}