{"id":"W3157439450","doi":"10.2196/25237","title":"Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Logistic regression; Glycated hemoglobin; Random forest; Artificial intelligence; Support vector machine; Multilayer perceptron; Computer science; Predictive modelling; Receiver operating characteristic; Health records; Preprint; Perceptron; Data mining; Artificial neural network; Medicine; Diabetes mellitus; Health care; Type 2 diabetes","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004404732,0.0008855412,0.0006539838,0.002014708,0.0002380101,0.001519431,0.0008233583,0.00084417,0.0007563572],"category_scores_gemma":[0.02105409,0.000308316,0.0005460529,0.001818289,0.0002233789,0.002046252,0.0007792625,0.0009136317,0.000239222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007297057,"about_ca_system_score_gemma":0.0008368535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01502403,"about_ca_topic_score_gemma":0.008776316,"domain_scores_codex":[0.9980995,0.00104743,0.0001667138,0.0003430874,0.0002798318,0.00006345667],"domain_scores_gemma":[0.9911646,0.006561577,0.0007788346,0.0004713555,0.0008887719,0.0001347448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004031852,0.0009654709,0.3934247,0.0002502993,0.0005239429,0.0001829773,0.0002454279,0.1416131,0.0007156231,0.001143949,0.002589118,0.4579423],"study_design_scores_gemma":[0.00003904098,0.0002630248,0.03980016,0.0001377112,0.0001213205,0.00009293696,0.0001336462,0.9552904,0.0009125772,0.00210097,0.001076318,0.00003188806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8322917,0.006439945,0.14779,0.004130512,0.0002227291,0.000390793,0.001951775,0.001084858,0.005697607],"genre_scores_gemma":[0.9223675,0.001550209,0.0744133,0.0002497205,0.0001253272,0.00007886627,0.0007508096,0.00001285175,0.000451434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01502403,"threshold_uncertainty_score":0.02987319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324685576049121,"score_gpt":0.289157379833488,"score_spread":0.2759105240729968,"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."}}