{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008409078,0.0001709212,0.0002717407,0.00009936834,0.0004333337,0.00002175101,0.0002152136,0.0002844376,0.0006692066],"category_scores_gemma":[0.0000713457,0.0001235675,0.00002904251,0.0001759049,0.00009974187,0.0002228143,0.00009269993,0.0003874607,0.0001693623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008243203,"about_ca_system_score_gemma":0.00008553449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004397825,"about_ca_topic_score_gemma":0.0001795352,"domain_scores_codex":[0.99748,0.0005085787,0.0005019128,0.0006808977,0.0001980473,0.0006305348],"domain_scores_gemma":[0.9983788,0.000256112,0.0002576383,0.000670713,0.00003544715,0.000401256],"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":[0.0007240694,0.0004187,0.2526526,0.0001823021,0.0002270356,0.00000856901,0.00443218,0.00002619697,0.0002034145,0.00128671,0.002418746,0.7374194],"study_design_scores_gemma":[0.01155871,0.0007014206,0.9162524,0.0002932667,0.0004316137,0.00003982837,0.00613799,0.009708418,0.000009126137,0.002554189,0.05157955,0.0007334793],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783836,0.008451518,0.006154271,0.001669312,0.00132314,0.0003627664,0.0001353459,0.0002045865,0.00331543],"genre_scores_gemma":[0.9960695,0.001990419,0.0003831747,0.000632819,0.0003848894,0.00001694422,0.0001068129,0.00003253002,0.0003829267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.736686,"threshold_uncertainty_score":0.7327343,"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."}}