{"id":"W4323241178","doi":"10.5220/0011628800003414","title":"Predicting Comorbidities in Diabetic Patients and Visualizing Data for Improved Healthcare","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Health care; Computer science; Data mining; Medicine; Data science","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":[0.0008763941,0.000637942,0.0005007052,0.003167334,0.000261214,0.002536555,0.0003512099,0.0006479189,0.002172033],"category_scores_gemma":[0.005900297,0.000172074,0.0006024928,0.002000054,0.0001410406,0.001107236,0.0006426472,0.0007002068,0.0003661423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004141469,"about_ca_system_score_gemma":0.0005833455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005588377,"about_ca_topic_score_gemma":0.007998887,"domain_scores_codex":[0.9996425,0.0001117426,0.00006876517,0.00005511858,0.00008329804,0.00003858341],"domain_scores_gemma":[0.997443,0.001459725,0.000381828,0.0002138105,0.0003129864,0.0001887494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001288894,0.0006780706,0.643024,0.0006047013,0.0003582609,0.0008257157,0.0008137282,0.01865743,0.01165332,0.003360458,0.02111355,0.2976218],"study_design_scores_gemma":[0.00017158,0.0007434235,0.397723,0.0008465685,0.0007810429,0.00237227,0.005237656,0.4935873,0.02527583,0.03777127,0.0352628,0.0002273086],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8486197,0.004775275,0.09547604,0.00694702,0.0005145049,0.0002725161,0.03179273,0.004121224,0.007481011],"genre_scores_gemma":[0.9281706,0.00161665,0.06057204,0.0003193245,0.0001725874,0.00004768757,0.008229316,0.00008149125,0.0007903553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005588377,"threshold_uncertainty_score":0.01111174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3372152808364619,"score_gpt":0.5335755142804626,"score_spread":0.1963602334440007,"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."}}