{"id":"W6920451332","doi":"10.60692/7cvjb-fa257","title":"A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Type 2 Diabetes Mellitus; Hidden Markov model; Diabetes mellitus; Missing data; Type 2 diabetes; Imputation (statistics); Gold standard (test); Clinical Practice; Markov model","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":[0.0007042371,0.0003997971,0.0005712089,0.0006178793,0.0003244965,0.0007871636,0.001079524,0.0008708527,0.001454165],"category_scores_gemma":[0.001701676,0.0003264093,0.0007041439,0.0005310719,0.0002663641,0.0004661112,0.0006458616,0.0008164844,0.000272475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005827372,"about_ca_system_score_gemma":0.0007848705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01785036,"about_ca_topic_score_gemma":0.01140889,"domain_scores_codex":[0.9997211,0.00007894924,0.00001872367,0.00008690498,0.0000591939,0.00003511754],"domain_scores_gemma":[0.9994325,0.0003465713,0.00005970313,0.00002723342,0.000102155,0.00003189574],"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":[0.00006918658,0.00003830817,0.00462643,0.0000376998,0.00005812697,0.00008069699,0.00006366034,0.9502412,0.0008528517,0.003321488,0.0003926432,0.04021777],"study_design_scores_gemma":[0.000001363308,0.000005912444,0.0001690297,0.00000152321,0.000003651371,0.000006404764,0.0000020346,0.9992078,0.00004661145,0.0004387485,0.0001151119,0.000001842047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06488387,0.0006041014,0.9316612,0.0003220851,0.00008698226,0.00004411966,0.0002523962,0.0004044384,0.001740703],"genre_scores_gemma":[0.8708735,0.0004651867,0.1229313,0.0001361078,0.00007642228,0.0001366325,0.0004664774,0.00003844883,0.004875966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01785036,"threshold_uncertainty_score":0.03549296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05876287377586813,"score_gpt":0.2598559589386435,"score_spread":0.2010930851627754,"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."}}