{"id":"W4412611645","doi":"10.1038/s41598-025-12310-1","title":"Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medical prescription; Predictive analytics; Diabetes mellitus; Analytics; Computer science; Personalized medicine; Machine learning; Data science; Medicine; Artificial intelligence; Bioinformatics; Pharmacology; Endocrinology; Biology","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":[0.003130032,0.001021172,0.0007993722,0.001846249,0.0004547674,0.001731655,0.001103912,0.0006895527,0.001239135],"category_scores_gemma":[0.0147948,0.0003714104,0.000735448,0.001880969,0.0003831109,0.001579582,0.001101451,0.001941139,0.0004543824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160284,"about_ca_system_score_gemma":0.002188523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01417842,"about_ca_topic_score_gemma":0.01473363,"domain_scores_codex":[0.9981098,0.0008534268,0.0001791179,0.000330333,0.0004413717,0.00008595984],"domain_scores_gemma":[0.991179,0.006686331,0.0006987971,0.0004836933,0.0007707469,0.0001814149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004921478,0.0007286845,0.04948149,0.0003760995,0.0002421696,0.0005407367,0.0004765495,0.6019686,0.002045358,0.01461474,0.009077382,0.319956],"study_design_scores_gemma":[0.00001814699,0.00003647822,0.001128163,0.00004602391,0.00002155076,0.00004770328,0.00004981323,0.9829641,0.0009004657,0.01317067,0.001599093,0.00001773481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09833812,0.003166586,0.8778995,0.005783297,0.0001678967,0.0003374395,0.003241603,0.004860135,0.006205388],"genre_scores_gemma":[0.8106554,0.00107842,0.1834936,0.0006848373,0.0001487543,0.0001796986,0.00290848,0.00009138691,0.0007594409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01417842,"threshold_uncertainty_score":0.0281918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09928478466856275,"score_gpt":0.4365445165248482,"score_spread":0.3372597318562854,"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."}}