{"id":"W2989324284","doi":"10.2196/15510","title":"Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients using Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; Major Research Plan; National Natural Science Foundation of China","keywords":"Medicine; Gradient boosting; Boosting (machine learning); Kidney disease; Retrospective cohort study; Receiver operating characteristic; Timeline; Diabetes mellitus; Artificial intelligence; Cohort; Machine learning; Computer science; Internal medicine; Random forest; Statistics","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":[0.004328914,0.000347877,0.0003937824,0.0006043254,0.0003455581,0.0005258206,0.0005137605,0.0004397895,0.0005389726],"category_scores_gemma":[0.00658181,0.0003146905,0.001017608,0.0006096192,0.000179776,0.0004249492,0.0004580886,0.0008654712,0.0001623178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003467817,"about_ca_system_score_gemma":0.0004959737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005645104,"about_ca_topic_score_gemma":0.005499905,"domain_scores_codex":[0.999216,0.0003439352,0.00006925142,0.0002104069,0.00009345906,0.00006698342],"domain_scores_gemma":[0.9975573,0.0007040233,0.0004545659,0.0006999219,0.0003684868,0.0002156033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000521966,0.0001738651,0.9925337,0.00001163216,0.00025972,0.00006904015,0.00007013539,0.001288301,0.0002864988,0.00009329646,0.0003609168,0.004330902],"study_design_scores_gemma":[0.0001710201,0.001221526,0.895271,0.00004156211,0.0008814279,0.0007936132,0.0003230918,0.09821013,0.0009402896,0.0005412836,0.001552209,0.00005281287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962226,0.0001638518,0.002865044,0.00004698722,0.0000121874,0.00002290571,0.0005539219,0.00001361305,0.0000987892],"genre_scores_gemma":[0.9973989,0.0001002922,0.001621296,0.00002683825,0.00001292153,0.00001929254,0.0007456607,0.000004099767,0.00007071664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005645104,"threshold_uncertainty_score":0.02289373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022035174913181,"score_gpt":0.2832313009389772,"score_spread":0.2730109491898454,"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."}}