{"id":"W7125586738","doi":"10.1109/ic3it66137.2025.11341169","title":"Machine Learning Meets Healthcare: Predicting Diabetes Onset With Ehr","year":2025,"lang":"","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Diabetes mellitus; Feature (linguistics); MEDLINE; Training set; Deep learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002603287,0.0009315636,0.001074221,0.0005690246,0.002790219,0.001550167,0.002189426,0.0004398479,0.0002682894],"category_scores_gemma":[0.001214842,0.0008213323,0.0002013443,0.003181988,0.0003814862,0.0007326256,0.001623216,0.003704262,0.0001081758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004240741,"about_ca_system_score_gemma":0.0021063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009130368,"about_ca_topic_score_gemma":0.002262583,"domain_scores_codex":[0.9908044,0.002162881,0.001403919,0.002229319,0.001210939,0.002188592],"domain_scores_gemma":[0.9945754,0.001565803,0.0006816079,0.001836876,0.0006500817,0.0006902193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005395931,0.0001069589,0.8694708,0.001393576,0.0001509567,0.00004355417,0.001649337,0.007056588,0.000006559803,0.01824858,0.0003829869,0.1014361],"study_design_scores_gemma":[0.001027047,0.001384645,0.06015906,0.001781324,0.0000609259,0.00003201323,0.0001178433,0.9120367,0.00009841478,0.0005067551,0.02208161,0.0007137093],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3277414,0.03075266,0.09362306,0.5029442,0.006160954,0.00386679,0.00006896501,0.004676706,0.03016525],"genre_scores_gemma":[0.9573955,0.0002294214,0.0282716,0.005777812,0.0001649657,0.00008206696,0.00003233863,0.00007813903,0.007968171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9049801,"threshold_uncertainty_score":0.9994863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117383861643784,"score_gpt":0.2765595318051533,"score_spread":0.2648211456407749,"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."}}