{"id":"W4399039824","doi":"10.1109/ispa59904.2024.10536796","title":"Constrained Linear Model Predictive Control for an Artificial Pancreas","year":2024,"lang":"en","type":"article","venue":"","topic":"Diabetes Management and Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Model predictive control; Artificial pancreas; Computer science; Controller (irrigation); Feed forward; Glycemic; Automation; Noise (video); Control theory (sociology); Control engineering; Control (management); Process (computing); Artificial intelligence; Engineering; Insulin; Diabetes mellitus; Type 1 diabetes","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":[0.000402448,0.0004808744,0.0004723195,0.0002368233,0.0002939148,0.0008968303,0.0006017757,0.0008585401,0.002173284],"category_scores_gemma":[0.001129031,0.0002343516,0.0003415989,0.0003323276,0.0004982425,0.000385489,0.0006122987,0.0008463927,0.0003209368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811175,"about_ca_system_score_gemma":0.0008002834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006745099,"about_ca_topic_score_gemma":0.003558954,"domain_scores_codex":[0.9997407,0.00006847599,0.00001357243,0.00005556874,0.00009958154,0.00002209286],"domain_scores_gemma":[0.9995994,0.0002137236,0.00004740895,0.0000213348,0.0001060923,0.00001203459],"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":[0.00009765982,0.00003652683,0.0002050964,0.0002233073,0.00002904528,0.0001747057,0.00005657731,0.9513399,0.007275878,0.006315006,0.00115642,0.03308994],"study_design_scores_gemma":[0.000009783947,0.00004170682,0.00006877827,0.000007461429,0.000005968374,0.00001307307,0.000003217146,0.9973539,0.0007897852,0.0007623381,0.0009392286,0.000004629444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01516199,0.000948794,0.975051,0.0003508951,0.0001847588,0.00006355454,0.00004895603,0.000538812,0.007651174],"genre_scores_gemma":[0.9376893,0.0006420041,0.05490465,0.0002078022,0.00007278797,0.0002211896,0.00009399633,0.00003975264,0.006128492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006745099,"threshold_uncertainty_score":0.0134117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05247035018695504,"score_gpt":0.350773290888645,"score_spread":0.2983029407016899,"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."}}