Abstract 12569: Analysis of Deep Learning Models for Prediction of Heart Failure Mortality

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Chayakrit Krittanawong ◽  
Kipp W Johnson ◽  
Usman Baber ◽  
Mehmet Aydar ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Validation Cohort ◽  
Sensitivity Analyses ◽  
Brier Score ◽  
Learning Models

Introduction: Heart failure (HF) is a leading cause of hospitalization, morbidity and mortality. Deep learning (DL) techniques appear to show promising results in risk stratification and prognosis in several conditions in medicine. However, few methods using DL exist to help quantitatively estimate prognosis of HF. We hypothesized that deep learning (DL) techniques could prognosis of HF using simple variables. We propose application of a custom-built deep-neural-network model to identify mortality in HF patients. Methods: Custom-built deep-neural-networks were assessed using survey data from 42,147 participants from the National Health and Nutrition Examination Survey 1999-2016 (NHANES). Variables were selected using clinical judgment and stepwise backward regressions to develop prediction models. We partitioned the data into training and testing sets and repetitive experiments. We then evaluated model performance based on discrimination and calibration including the area under the receiver-operator characteristics curve (C-statistics), balanced accuracy, probability calibration with sigmoid, and the Brier score, respectively. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We validated models’ performance using Mount Sinai database. Results: Of 42,147 participants with 4,060 variables, 1,491 (3.5%) had HF and HF mortality was 51.8%. In validation cohort, of 26,333 HF patients, the mortality in HF patients was 405 (1.5%). Final model using only 20 variables (age, race, gender, BMI, smoking, alcohol consumption, HTN, COPD, SBP, DBP, HR, HDL, LDL, CRP, A1C, BUN, creatinine, hemoglobin, sodium level, on statin) was tested. A state-of-the-art deep learning models achieved high accuracy for predicting mortality in HF patients with an AUC of 0.96 (95% CI: 0.95-0.99) in the first cohort and AUC of 0.93 (95% CI: 0.91-0.96) in validation cohort. Conclusions: A deep neural network model has shown to have high predictive accuracy and discriminative and calibrative power for prediction of HF mortality. Further research can delineate the clinical implications of DL in predicting HF mortality.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

scholarly journals Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

2021 ◽  
Vol 72 (1) ◽  
pp. 11-20
Author(s):  
Mingtao He ◽  
Wenying Li ◽  
Brian K. Via ◽  
Yaoqi Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Price Volatility ◽  
Google Trends ◽  
Learning Models ◽  
Fresh Perspective ◽  
Futures Price

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

scholarly journals Image Classification Using Transfer Learning and Deep Learning

2021 ◽  
Vol 10 (9) ◽  
pp. 25394-25398
Author(s):  
Chitra Desai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network ◽  
Visual Recognition ◽  
Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used.   This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

scholarly journals Comparisons of different deep learning-based methods on fault diagnosis for geared system

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816 ◽  
Author(s):  
Bing Han ◽  
Xiaohui Yang ◽  
Yafeng Ren ◽  
Wanggui Lan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Mechanical System ◽  
Network Model ◽  
Learning Theory ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Gear Fault

The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

Deep learning-based inertia tensor identification of the combined spacecraft

2020 ◽  
Vol 234 (7) ◽  
pp. 1356-1366
Author(s):  
Weimeng Chu ◽  
Shunan Wu ◽  
Xiao He ◽  
Yufei Liu ◽  
Zhigang Wu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Angular Rate ◽  
Inertia Tensor ◽  
Measurement Noise ◽  
Identification Performance ◽  
Operating Environments

The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.

scholarly journals Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Jinying Kong ◽  
Yating Yang ◽  
Lei Wang ◽  
Xi Zhou ◽  
Tonghai Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Statistical Machine Translation ◽  
Learning Problem ◽  
Proposed Model ◽  
Discriminative Model

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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