{"id":"W4405489011","doi":"10.1109/embc53108.2024.10782210","title":"Predicting Time to Diabetes Diagnosis Using Random Survival Forests","year":2024,"lang":"en","type":"article","venue":"","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Public Health Ontario; University of Toronto","funders":"","keywords":"Random forest; Computer science; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005177249,0.0006392006,0.0006298294,0.001116411,0.0003098524,0.000535862,0.0006810325,0.0005506788,0.001124537],"category_scores_gemma":[0.0102902,0.0002406046,0.0009359836,0.00059256,0.0002064522,0.0005147709,0.000367997,0.0007542719,0.0003984191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005914327,"about_ca_system_score_gemma":0.001110833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02710391,"about_ca_topic_score_gemma":0.03174793,"domain_scores_codex":[0.9991006,0.0004738902,0.00005318097,0.0001810823,0.00008030057,0.0001109365],"domain_scores_gemma":[0.9936128,0.004910537,0.0004061452,0.0003161793,0.0005862208,0.0001681406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006795304,0.0002350253,0.1690697,0.00006109592,0.0002682714,0.0002614267,0.00008770335,0.7152804,0.0006747518,0.001200638,0.003335555,0.1088459],"study_design_scores_gemma":[0.00002093941,0.00006779442,0.005363359,0.00001102859,0.00002527048,0.0000444806,0.0000143501,0.992516,0.0002044715,0.001477146,0.0002450641,0.00000999552],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7433449,0.0008853232,0.2490449,0.0008721213,0.0001686091,0.000194752,0.002759137,0.001299867,0.001430452],"genre_scores_gemma":[0.9648117,0.0001499111,0.03249067,0.0001070269,0.00005331209,0.00005011777,0.001829345,0.0000251523,0.0004827075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02710391,"threshold_uncertainty_score":0.05389225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673834341170521,"score_gpt":0.2615193099807971,"score_spread":0.2447809665690919,"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."}}