{"id":"W3177956460","doi":"10.3390/app10186350","title":"In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Diabetes Management and Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Blood sugar regulation; Computer science; Artificial intelligence; Machine learning; Reinforcement; Control (management); Task (project management); Diabetes mellitus; Medicine; Engineering","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":[0.001270466,0.0005940265,0.0006399229,0.0002419581,0.0001982424,0.0005022776,0.0005808314,0.001023588,0.001206899],"category_scores_gemma":[0.004339361,0.0001921982,0.0004701864,0.0001904106,0.0005353601,0.0002672338,0.000544177,0.0008903353,0.0001357359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006977341,"about_ca_system_score_gemma":0.0008153878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008042484,"about_ca_topic_score_gemma":0.003177085,"domain_scores_codex":[0.9996403,0.0001643926,0.00001906577,0.00005497641,0.00005536101,0.0000658289],"domain_scores_gemma":[0.997239,0.002048213,0.000214289,0.00008238955,0.0002158443,0.0002002204],"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":[0.0004822128,0.0003115734,0.003777678,0.00008807042,0.00003640323,0.00009918989,0.00001919318,0.9887371,0.0009310074,0.0007323603,0.0003686227,0.004416607],"study_design_scores_gemma":[0.0001179519,0.000340812,0.0008877153,0.00001095962,0.00001429709,0.00001997455,0.00001473253,0.9966581,0.001310549,0.0004611476,0.0001560425,0.000007688735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527765,0.0007642988,0.04047455,0.0009003452,0.0001488016,0.0001158885,0.0003633784,0.0003731565,0.004083067],"genre_scores_gemma":[0.9956111,0.00008827591,0.003684761,0.00009562998,0.000006456566,0.00002890769,0.0001299687,0.00000673841,0.0003482754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008042484,"threshold_uncertainty_score":0.01599133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06031170394336045,"score_gpt":0.3337955022272621,"score_spread":0.2734837982839016,"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."}}