{"id":"W2962974300","doi":"10.2196/13139","title":"Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dementia; Proportional hazards model; Hazard ratio; Medicine; Population; Cohort; Deep learning; Cohort study; Gerontology; Artificial intelligence; Algorithm; Machine learning; Computer science; Internal medicine; Confidence interval; Disease; Environmental health","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":[0.02586881,0.001072595,0.001472473,0.001432241,0.0003839777,0.001119746,0.001160259,0.001048716,0.001054225],"category_scores_gemma":[0.039913,0.0004133106,0.002490796,0.0008838329,0.0006218703,0.001638755,0.001001388,0.002499482,0.0001822868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007690573,"about_ca_system_score_gemma":0.001700533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008316872,"about_ca_topic_score_gemma":0.003776795,"domain_scores_codex":[0.9950263,0.003336418,0.0002436205,0.0006855322,0.0004473868,0.0002606992],"domain_scores_gemma":[0.9611531,0.03326033,0.001693434,0.001638948,0.001534507,0.0007195432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008255552,0.0007544669,0.7421963,0.0003046424,0.003877329,0.0003821021,0.0001754276,0.1422339,0.0009318561,0.001774645,0.002122983,0.09699071],"study_design_scores_gemma":[0.0001798946,0.0008414476,0.04735084,0.00005382045,0.0007666065,0.0001216205,0.00007327536,0.9481905,0.0005012837,0.00158523,0.0002971677,0.0000382734],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500262,0.002875814,0.04363115,0.001650625,0.0002026052,0.0001518321,0.0005051968,0.0001584609,0.000798051],"genre_scores_gemma":[0.9918391,0.0004185502,0.006794298,0.0001422227,0.00006894585,0.00005319807,0.0003175011,0.00001525194,0.0003508742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02586881,"threshold_uncertainty_score":0.1368089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809355727452807,"score_gpt":0.3230499600294193,"score_spread":0.3049564027548913,"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."}}