{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002089722,0.0007463323,0.0007694975,0.002445885,0.0002620276,0.001788082,0.001133628,0.0009551697,0.001627241],"category_scores_gemma":[0.01130294,0.0003306788,0.0007753515,0.00224562,0.0002238735,0.001985287,0.00136302,0.001824427,0.001163261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008741632,"about_ca_system_score_gemma":0.001391495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008873468,"about_ca_topic_score_gemma":0.01272525,"domain_scores_codex":[0.998811,0.0003978798,0.0001275826,0.0003376252,0.0002426593,0.0000832363],"domain_scores_gemma":[0.995962,0.002143474,0.0005265829,0.0004912629,0.0007124959,0.0001642887],"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":[0.001059565,0.0009693642,0.13438,0.001077892,0.000485976,0.001004508,0.0005432156,0.159607,0.003643786,0.01354588,0.03900565,0.6446772],"study_design_scores_gemma":[0.00005088311,0.0001341176,0.01678519,0.0003348778,0.0001852157,0.0003266552,0.000282108,0.9336783,0.004457945,0.0274663,0.01622356,0.0000748066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.216215,0.01132394,0.67889,0.02062796,0.0007459521,0.0005774814,0.05127882,0.006993255,0.01334748],"genre_scores_gemma":[0.8077149,0.004073322,0.1542997,0.001677597,0.0005380437,0.0002342739,0.02868207,0.0001448966,0.002635174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008873468,"threshold_uncertainty_score":0.01764363,"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."}}