An attempt of applying the Lagrange-type 1-step-ahead numerical differentiation method to optimize the SGD algorithm in deep learning

2021 ◽  
Author(s):  
Enhong Liu ◽  
Dan Su ◽  
Liangming Chen ◽  
Long Jin ◽  
Xiuchun Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Numerical Differentiation ◽  
Differentiation Method

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

scholarly journals Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity

2018 ◽  
Vol 103 (5) ◽  
pp. 580-584 ◽  
Author(s):  
Travis K Redd ◽  
John Peter Campbell ◽  
James M Brown ◽  
Sang Jin Kim ◽  
Susan Ostmo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Retinopathy Of Prematurity ◽  
Predictive Value ◽  
Severity Score ◽  
Receiver Operating Curve ◽  
Fundus Photography ◽  
Image Analysis System ◽  
Reference Standard ◽  
Type 1 Rop

BackgroundPrior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.MethodsClinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1–9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.Results4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001).ConclusionThe i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.

A Deep Learning-enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome (Preprint)

2020 ◽  
Author(s):  
Chih-Min Liu ◽  
Chien-Liang Liu ◽  
Kai-Wen Hu ◽  
Vincent S. Tseng ◽  
Shih-Lin Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brugada Syndrome ◽  
Area Under The Curve ◽  
Early Time ◽  
Learning Model ◽  
Kappa Coefficient ◽  
Mcnemar Test ◽  
Inherited Arrhythmia ◽  
Deep Learning Model

BACKGROUND Brugada syndrome is a rare inherited arrhythmia with a unique electrocardiogram (ECG) pattern (type 1 Brugada ECG pattern), which is a major cause of sudden cardiac death in young people. Automatic screening for the ECG pattern of Brugada syndrome by a deep learning model gives us the chance to identify these patients at an early time, thus allowing them to receive life-saving therapy. OBJECTIVE To develop a deep learning-enabled ECG model for diagnosing Brugada syndrome. METHODS A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for one to one allocation) were extracted from the hospital-based ECG database for a two-stage analysis with a deep learning model. We first trained the network to identify right bundle branch block (RBBB) pattern, and then, we transferred the first-stage learning to the second task to diagnose the type 1 Brugada ECG pattern. The diagnostic performance of the deep learning model was compared to that of board-certified practicing cardiologists. The model was also validated by the independent international data of ECGs. RESULTS The AUC (area under the curve) of the deep learning model in diagnosing the type 1 Brugada ECG pattern was 0.96 (sensitivity: 88.4%, specificity: 89.1%). The sensitivity and specificity of the cardiologists for the diagnosis of the type 1 Brugada ECG pattern were 62.7±17.8%, and 98.5±3.0%, respectively. The diagnoses by the deep learning model were highly consistent with the standard diagnoses (Kappa coefficient: 0.78, McNemar test, P = 0.86). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (Kappa coefficient: 0.60, McNemar test, P = 2.35x10-22). For the international validation, the AUC of the deep learning model for diagnosing the type 1 Brugada ECG pattern was 0.99 (sensitivity: 85.7%, specificity: 100.0%). CONCLUSIONS The deep learning-enabled ECG model for diagnosing Brugada syndrome is a robust screening tool with better diagnostic sensitivity than that of cardiologists.

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
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