Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning

• URL: http://arxiv.org/abs/2007.11880v1
• Date: Thu, 23 Jul 2020 09:38:54 GMT
• Title: Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning
• Authors: Fr\'ed\'eric Log\'e (CMAP), Erwan Le Pennec (XPOP, CMAP), Habiboulaye Amadou-Boubacar
• Abstract summary: Patients with diabetes who are self-monitoring have to decide right before each meal how much insulin they should take. We challenged this rule applying Reinforcement Learning techniques on data simulated with T1DM, an FDA-approved simulator.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Patients with diabetes who are self-monitoring have to decide right before each meal how much insulin they should take. A standard bolus advisor exists, but has never actually been proven to be optimal in any sense. We challenged this rule applying Reinforcement Learning techniques on data simulated with T1DM, an FDA-approved simulator developed by Kovatchev et al. modeling the gluco-insulin interaction. Results show that the optimal bolus rule is fairly different from the standard bolus advisor, and if followed can actually avoid hypoglycemia episodes.

Related papers

• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [50.80532910808962]
  We present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture. GluFormer generalizes to 15 different external datasets, including 4936 individuals across 5 different geographical regions. It can also predict onset of future health outcomes even 4 years in advance.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting [48.47935559597376]
  Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels. Most current DR grading methods suffer from insufficient robustness to data variability. We propose a novel DR grading framework CLIP-DR based on three observations.
  arXiv  Detail & Related papers  (2024-07-04T17:14:18Z)
• Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients [0.0]
  This study aimed to develop a supervised learning model to predict the risk of developing albuminuria in T2DM patients. It consisted of 10 attributes as features and 1 attribute as the target (albuminuria) It achieved accuracy and f1-score values as high as 0.74 and 0.75, respectively, making it suitable for screening purposes in predicting albuminuria in T2DM.
  arXiv  Detail & Related papers  (2023-09-28T08:41:12Z)
• Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments [0.40792653193642503]
  People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime. We propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy.
  arXiv  Detail & Related papers  (2023-09-17T01:34:02Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction [37.846368153741395]
  Task-wise Split Gradient Boosting Trees (TSGB) is proposed for the multi-center diabetes prediction task. TSGB achieves superior performance against several state-of-the-art methods. The proposed TSGB method has been deployed as an online diabetes risk assessment software for early diagnosis.
  arXiv  Detail & Related papers  (2021-08-16T14:22:44Z)
• Using Machine Learning Techniques to Identify Key Risk Factors for Diabetes and Undiagnosed Diabetes [0.0]
  This paper reviews a wide selection of machine learning models built to predict the presence of diabetes and the presence of undiagnosed diabetes. The most relevant variables of the best performing models are then compared. Blood osmolality, family history, the prevalance of various compounds, and hypertension are key indicators for all diabetes risk.
  arXiv  Detail & Related papers  (2021-05-19T20:02:35Z)
• LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management [4.01573226844961]
  We introduce an LSTM-based approach to blood glucose level prediction aimed at "what if" scenarios. We then derive a novel architecture for the same recommendation task. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach.
  arXiv  Detail & Related papers  (2021-03-06T19:06:14Z)
• Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation [16.93692520921499]
  We propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In the adult cohort, percentage time in target range improved from 77.6% to 80.9% with single-hormone control. In the adolescent cohort, percentage time in target range improved from 55.5% to 65.9% with single-hormone control.
  arXiv  Detail & Related papers  (2020-05-18T20:13:16Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.