{"id":"W4390143137","doi":"10.18280/isi.280607","title":"Predictive Modelling of Glycated Hemoglobin Levels Using Machine Learning Regressors","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glycated hemoglobin; Machine learning; Econometrics; Artificial intelligence; Hemoglobin; Computer science; Economics; Internal medicine; Diabetes mellitus; Medicine; Endocrinology; 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":[0.002053296,0.0008421034,0.0007007472,0.0007867707,0.0002256047,0.001020916,0.0006897752,0.0007423919,0.0009047674],"category_scores_gemma":[0.00527468,0.0003755272,0.0008848002,0.0006495181,0.0002438249,0.0006304038,0.0003194012,0.001212348,0.0004476311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007013688,"about_ca_system_score_gemma":0.000900137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02007996,"about_ca_topic_score_gemma":0.008995604,"domain_scores_codex":[0.9994285,0.0002476434,0.00003840453,0.0001139395,0.0001101598,0.000061311],"domain_scores_gemma":[0.9973021,0.002001252,0.0002296242,0.00007693289,0.0003600245,0.00003020497],"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":[0.0001086109,0.00009351392,0.008904931,0.00004897151,0.00008988078,0.00007511196,0.0000406064,0.9614419,0.001364122,0.0005817332,0.0003848652,0.02686572],"study_design_scores_gemma":[0.000002201709,0.00001538593,0.0006672334,0.000004479507,0.000005820146,0.000005541919,0.000002752811,0.9988532,0.0002498525,0.0001316717,0.00005846367,0.000003331263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4021879,0.00187629,0.5897501,0.0007584961,0.0001880233,0.0001110429,0.000565554,0.001807617,0.002754887],"genre_scores_gemma":[0.9687838,0.0005719954,0.02847629,0.00005346408,0.00004349033,0.00007545773,0.0003943514,0.00003159315,0.001569526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02007996,"threshold_uncertainty_score":0.03992617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1882080874137074,"score_gpt":0.3980303475232409,"score_spread":0.2098222601095335,"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."}}