{"id":"W2996533187","doi":"10.1007/978-3-030-32587-9_7","title":"Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach","year":2019,"lang":"en","type":"book-chapter","venue":"Studies in big data","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Type 2 Diabetes Mellitus; Diabetes mellitus; Medicine; Type 2 diabetes; Machine learning; Artificial intelligence; Computer science; Endocrinology","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":[0.0005867341,0.0005204761,0.0005187928,0.0008935683,0.000215584,0.0006414127,0.0007880672,0.0005808015,0.002736692],"category_scores_gemma":[0.001160378,0.0001827196,0.0008865003,0.0005722059,0.00008911823,0.0004925069,0.0001970646,0.0006759578,0.0007249401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004031926,"about_ca_system_score_gemma":0.0004202284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01175029,"about_ca_topic_score_gemma":0.008335534,"domain_scores_codex":[0.9998773,0.00002761588,0.0000103357,0.00004456422,0.00001795682,0.00002211243],"domain_scores_gemma":[0.9993752,0.0004306935,0.00004186514,0.00002460662,0.0001076394,0.00002009414],"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":[0.00057935,0.0008466961,0.2349708,0.000216386,0.0006819397,0.0004524715,0.0001509754,0.4624363,0.001630977,0.004825893,0.01695541,0.2762527],"study_design_scores_gemma":[0.000007948785,0.00004253813,0.01286004,0.00001782718,0.00006432738,0.00005877745,0.00002770121,0.9850031,0.000204695,0.00131821,0.000386372,0.000008629534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6298793,0.003374114,0.3446251,0.002455664,0.0005528782,0.0001405125,0.007425893,0.002142805,0.009403687],"genre_scores_gemma":[0.9585291,0.0006959112,0.02924936,0.0001371486,0.0001810873,0.0001111911,0.004400632,0.00003419993,0.00666151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01175029,"threshold_uncertainty_score":0.02336377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7036060933300636,"score_gpt":0.5165066646733827,"score_spread":0.1870994286566809,"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."}}