{"id":"W3120457190","doi":"10.1016/j.cmpb.2021.105936","title":"Long-term use of the hybrid artificial pancreas by adjusting carbohydrate ratios and programmed basal rate: A reinforcement learning approach","year":2021,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Pancreatic function and diabetes","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canada Research Chairs","keywords":"Reinforcement learning; Basal (medicine); Reinforcement; Term (time); Computer science; Artificial intelligence; Psychology; Internal medicine; Medicine; Insulin; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006667751,0.0003190904,0.0004207888,0.0001627412,0.0001488968,0.0004695501,0.0005274417,0.0003332781,0.0006263306],"category_scores_gemma":[0.0009340126,0.0001103931,0.0002701308,0.0001095479,0.0002606274,0.0002893205,0.00040228,0.0003770771,0.0001111754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002380902,"about_ca_system_score_gemma":0.0002915902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009780658,"about_ca_topic_score_gemma":0.0007605325,"domain_scores_codex":[0.9998657,0.00005034865,0.00001053808,0.00003039204,0.00002794223,0.00001523053],"domain_scores_gemma":[0.9996805,0.0001479392,0.00004627333,0.0000365833,0.00005148564,0.00003727362],"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":[0.006018271,0.00319077,0.02069076,0.0002670388,0.0004904277,0.0004286632,0.0002546744,0.4701536,0.1101264,0.002841616,0.0006669206,0.3848708],"study_design_scores_gemma":[0.0001886504,0.003198028,0.006212725,0.0000266751,0.0001995345,0.0002612681,0.00005777938,0.9618999,0.02543548,0.001471272,0.001010073,0.00003854928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7905089,0.000516939,0.2064664,0.0001987217,0.00005051686,0.00007910976,0.00003693194,0.0002607178,0.001881822],"genre_scores_gemma":[0.9893498,0.0000822228,0.01007507,0.00002099564,0.000005290001,0.00003276447,0.00001277342,0.000007283671,0.0004136735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009780658,"threshold_uncertainty_score":0.00352633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06841030764769254,"score_gpt":0.3218691429603892,"score_spread":0.2534588353126966,"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."}}