{"id":"W4415169415","doi":"10.1007/s10916-025-02278-w","title":"Predicting All-Cause Mortality in Diabetic Patients 2 Years in Advance Using Aggregated EHR Data and Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Health informatics; Disease; Resource allocation; Predictive modelling; Health care; Emergency department; Informatics; MEDLINE","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001223987,0.0004086792,0.000620508,0.001092753,0.0002488925,0.0009181817,0.0003593409,0.0006342055,0.0006655412],"category_scores_gemma":[0.006390736,0.0001914505,0.0007548166,0.001094709,0.000102185,0.0006652612,0.0005130845,0.0008867786,0.0002627466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004705729,"about_ca_system_score_gemma":0.000705537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01384326,"about_ca_topic_score_gemma":0.0189519,"domain_scores_codex":[0.9994631,0.0001538749,0.00007690837,0.0001165764,0.00007900049,0.0001104615],"domain_scores_gemma":[0.9969784,0.001362621,0.0006297015,0.0002623028,0.0004367196,0.0003302494],"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":[0.0006127312,0.0002496054,0.9838735,0.00001595622,0.0001485029,0.00009420867,0.00003217764,0.005657453,0.0002310433,0.00007126852,0.0005835445,0.00843009],"study_design_scores_gemma":[0.00005757526,0.0004994311,0.866206,0.00002439162,0.0002603319,0.0002595367,0.0002821019,0.1304557,0.0006773258,0.0005376582,0.0007138557,0.00002613392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959739,0.000164445,0.0008887046,0.0001954095,0.00002806102,0.000007629366,0.002348033,0.0000279245,0.0003660577],"genre_scores_gemma":[0.9949433,0.00007554697,0.0007656013,0.00003196521,0.00003095623,0.000005325117,0.004016719,0.000002351239,0.0001281637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01384326,"threshold_uncertainty_score":0.02752537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0566886838156516,"score_gpt":0.3649545467764447,"score_spread":0.3082658629607931,"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."}}