{"id":"W4206829205","doi":"10.1109/access.2021.3133700","title":"A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kidney disease; Medicine; Context (archaeology); Disease; Diabetes mellitus; Medical record; Renal function; Ranking (information retrieval); Feature (linguistics); Term (time); Artificial intelligence; Intensive care medicine; Computer science; Machine learning; 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":[0.002407853,0.000435434,0.0006455539,0.004793142,0.0005109257,0.001044033,0.0004909826,0.000857057,0.0009298698],"category_scores_gemma":[0.01648224,0.0001174673,0.0007540499,0.003853802,0.0002169228,0.0006348923,0.0004657859,0.0006448793,0.0004930489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006129465,"about_ca_system_score_gemma":0.001177168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01160666,"about_ca_topic_score_gemma":0.01304738,"domain_scores_codex":[0.9973574,0.000827918,0.0004782555,0.0005935757,0.0005602684,0.0001825471],"domain_scores_gemma":[0.9895155,0.005647245,0.001793901,0.001043909,0.001697527,0.0003017893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000587123,0.0007299134,0.8893574,0.0002218739,0.0004698714,0.0006469293,0.0002893772,0.009010592,0.00186386,0.000381249,0.004483065,0.09195872],"study_design_scores_gemma":[0.0000431156,0.0006477349,0.8191922,0.00009798939,0.0002610507,0.001038094,0.0006371121,0.1692263,0.002902307,0.001109485,0.004789875,0.00005466746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664533,0.001017825,0.01035414,0.001238143,0.00008752702,0.0001853969,0.01843212,0.0004084494,0.001822979],"genre_scores_gemma":[0.971001,0.0002307956,0.01305321,0.0001278752,0.0000941129,0.00007129336,0.01500612,0.00000837259,0.0004071616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01160666,"threshold_uncertainty_score":0.0230782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07544052414019527,"score_gpt":0.4171521250970025,"score_spread":0.3417116009568072,"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."}}