{"id":"W4388982272","doi":"10.2196/49113","title":"A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study","year":2023,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Multimedia University","keywords":"Decision tree; Linear regression; Medicine; Diabetes mellitus; Regression analysis; Population; Regression; Artificial intelligence; Machine learning; Statistics; Computer science; Environmental health; Mathematics; Endocrinology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001907378,0.0004166828,0.0007126834,0.0007816573,0.002163177,0.00006087559,0.0002322743,0.0002400392,0.0001203508],"category_scores_gemma":[0.00100298,0.0003943147,0.0001045267,0.001267,0.0001176811,0.0002886204,0.0002524974,0.001471389,0.0007230122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000161811,"about_ca_system_score_gemma":0.000599607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008870857,"about_ca_topic_score_gemma":0.001125469,"domain_scores_codex":[0.9950674,0.001276697,0.001277257,0.0005546006,0.0005350028,0.001289018],"domain_scores_gemma":[0.9960637,0.001954821,0.0005068544,0.0005506347,0.0002763313,0.0006476388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005126276,0.0003193633,0.9588304,0.001252279,0.00006075301,0.000172571,0.02513621,0.00007690752,0.00003075318,0.00001102937,0.0006955944,0.01336286],"study_design_scores_gemma":[0.003030162,0.01186205,0.6652326,0.002952732,0.0002108646,0.00001774576,0.097621,0.2129907,0.0001879981,0.0001773569,0.004233663,0.001483137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988007,0.0001757859,0.000004532414,0.004659771,0.0005104446,0.005385874,0.0004095065,0.0007733973,0.00007365333],"genre_scores_gemma":[0.9916974,0.0001113409,0.00005571387,0.004651602,0.00030452,0.002740797,0.0002943084,0.00006641123,0.0000779613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2935978,"threshold_uncertainty_score":0.9998509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05677314022216912,"score_gpt":0.3814811412672563,"score_spread":0.3247080010450872,"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."}}