{"id":"W4405803752","doi":"10.62762/tis.2025.367320","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","year":2024,"lang":"en","type":"article","venue":"ICCK Transactions on Intelligent Systematics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Health records; Diabetes mellitus; Electronic health record; Medical record; Medicine; Data science; Computer science; Political science; Health care; Internal medicine","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"],"consensus_categories":[],"category_scores_codex":[0.001767434,0.0003008062,0.0004621065,0.0004328963,0.000440842,0.0004221957,0.0009788419,0.000109179,0.00002576204],"category_scores_gemma":[0.0000850513,0.0002646079,0.0001100128,0.0007950139,0.00004456353,0.0003099666,0.00003234032,0.0008666394,0.0001707143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003830897,"about_ca_system_score_gemma":0.0005596554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001799246,"about_ca_topic_score_gemma":0.0004353818,"domain_scores_codex":[0.9965099,0.0008042931,0.0008813079,0.0008363836,0.0003659485,0.0006022348],"domain_scores_gemma":[0.9968536,0.001293951,0.0002184041,0.001308131,0.0001034227,0.0002224339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002732902,0.001311322,0.01137781,0.08592831,0.00146533,0.00002217511,0.01838968,0.1403622,0.00001984205,0.02515383,0.001768842,0.7141733],"study_design_scores_gemma":[0.00007370641,0.0004042495,0.00004848704,0.002865046,0.00005822192,0.000009443982,0.0001353522,0.992016,0.0002341488,0.0002764584,0.003645013,0.0002338936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006218902,0.005205919,0.9835355,0.001837063,0.001178692,0.001112593,0.0000758321,0.0007617541,0.00007371273],"genre_scores_gemma":[0.973267,0.0008497738,0.0252793,0.0001147858,0.00008219247,0.0001798164,0.00002177833,0.00004907223,0.0001562747],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9670481,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04407328744874354,"score_gpt":0.3346700862326227,"score_spread":0.2905967987838792,"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."}}