scholarly journals Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes

2016 ◽  
Author(s):  
Phyo Phyo San ◽  
Sai Ho Ling ◽  
Hung T. Nguyen
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Learning Framework

scholarly journals An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5058 ◽  
Author(s):  
Taiyu Zhu ◽  
Kezhi Li ◽  
Lei Kuang ◽  
Pau Herrero ◽  
Pantelis Georgiou
Keyword(s):  
Type 1 Diabetes ◽  
Reinforcement Learning ◽  
Glucose Monitoring ◽  
Insulin Delivery ◽  
Target Range ◽  
Average Percentage ◽  
Learning Framework ◽  
Policy Gradient ◽  
Bolus Insulin

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70–180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.

546-P: Deep Learning Predictions of Diabetic Retinopathy Associated with Progression of Renal Disease in Type 1 Diabetes

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 546-P
Author(s):  
JOSEPH C. MELLOR ◽  
AMOS J. STORKEY ◽  
HELEN COLHOUN ◽  
PAUL M. MCKEIGUE ◽  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Renal Disease ◽  
Progression Of Renal Disease

scholarly journals A multi-task convolutional deep learning method for HLA allelic imputation and its application to trans-ethnic MHC fine-mapping of type 1 diabetes

2020 ◽  
Author(s):  
Tatsuhiko Naito ◽  
Ken Suzuki ◽  
Jun Hirata ◽  
Yoichiro Kamatani ◽  
Koichi Matsuda ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Fine Mapping ◽  
Low Frequency ◽  
Amino Acid Position ◽  
Genotype Imputation ◽  
Learning Method ◽  
Frequency Spectra ◽  
Shared Risk

Conventional HLA imputation methods drop their performance for infrequent alleles, which reduces reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,112), DEEP*HLA achieved the highest accuracies in both datasets (0.987 and 0.976) especially for low-frequency and rare alleles. DEEP*HLA was less dependent of distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We applied DEEP*HLA to type 1 diabetes GWAS data of BioBank Japan (n = 62,387) and UK Biobank (n = 356,855), and successfully disentangled independently associated class I and II HLA variants with shared risk between diverse populations (the top signal at HLA-DRβ1 amino acid position 71; P = 6.2 × 10-119). Our study illustrates a value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

scholarly journals A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tatsuhiko Naito ◽  
Ken Suzuki ◽  
Jun Hirata ◽  
Yoichiro Kamatani ◽  
Koichi Matsuda ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Fine Mapping ◽  
Human Leukocyte ◽  
Amino Acid Position ◽  
Genotype Imputation ◽  
Learning Method ◽  
Frequency Spectra ◽  
Shared Risk

AbstractConventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1;P = 7.5 × 10−120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

scholarly journals Deep learning-based pancreas volume assessment in individuals with type 1 diabetes

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Raphael Roger ◽  
Melissa A. Hilmes ◽  
Jonathan M. Williams ◽  
Daniel J. Moore ◽  
Alvin C. Powers ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Deep Learning ◽  
Volume Measurement ◽  
Training Data ◽  
Human Pancreas ◽  
Abdominal Magnetic Resonance Imaging ◽  
Clinical Databases ◽  
Mri Scans ◽  
Pancreas Volume

AbstractPancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). Studies investigating pancreas volume are underway to assess pancreas volume in large clinical databases and studies, but manual pancreas annotation is time-consuming and subjective, preventing extension to large studies and databases. This study develops deep learning for automated pancreas volume measurement in individuals with diabetes. A convolutional neural network was trained using manual pancreas annotation on 160 abdominal magnetic resonance imaging (MRI) scans from individuals with T1D, controls, or a combination thereof. Models trained using each cohort were then tested on scans of 25 individuals with T1D. Deep learning and manual segmentations of the pancreas displayed high overlap (Dice coefficient = 0.81) and excellent correlation of pancreas volume measurements (R2 = 0.94). Correlation was highest when training data included individuals both with and without T1D. The pancreas of individuals with T1D can be automatically segmented to measure pancreas volume. This algorithm can be applied to large imaging datasets to quantify the spectrum of human pancreas volume.

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