{"id":"W2546819379","doi":"10.2196/mhealth.6562","title":"Sleep Quality Prediction From Wearable Data Using Deep Learning","year":2016,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Sleep and related disorders","field":"Psychology","cited_by":244,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deep learning; Actigraphy; Computer science; Artificial intelligence; Machine learning; Wearable computer; Convolutional neural network; Raw data; Sleep (system call); Activity recognition; 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":[0.0006403563,0.00104689,0.0005883754,0.001096075,0.0001689322,0.0004593729,0.0008337275,0.0005525855,0.001089481],"category_scores_gemma":[0.002538463,0.0002966374,0.0007701093,0.0009376018,0.0001936337,0.0005907397,0.0005592022,0.0008670605,0.00037323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005997333,"about_ca_system_score_gemma":0.0004922666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01212206,"about_ca_topic_score_gemma":0.01464514,"domain_scores_codex":[0.9997342,0.00005450243,0.00002814737,0.00009369464,0.00004243212,0.00004706116],"domain_scores_gemma":[0.9992307,0.0003364703,0.0001254316,0.00006313799,0.0001982745,0.00004590623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001107546,0.001402388,0.1240836,0.0005322075,0.0005833167,0.0004329562,0.0001263029,0.4820034,0.007304432,0.0006093543,0.008314209,0.3735003],"study_design_scores_gemma":[0.00001913241,0.000101475,0.01080964,0.00003255296,0.00003061076,0.00003599362,0.00001943164,0.9870821,0.0009274249,0.0006184417,0.0003145196,0.000008628185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7589389,0.005234807,0.2214147,0.001088718,0.0003007546,0.0002204794,0.007277409,0.002459657,0.003064586],"genre_scores_gemma":[0.9753725,0.0007273602,0.01834431,0.0001489866,0.00006873789,0.00009222933,0.004205493,0.00002628105,0.001014107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01212206,"threshold_uncertainty_score":0.02410299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1129966129913884,"score_gpt":0.4252524601412962,"score_spread":0.3122558471499077,"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."}}