Deep learning for healthcare decision making with EMRs

Liang, Znaonui; Zhang, Gang; Huang, Jimmy Xiangji; Hu, Qmming Vivian

doi:10.1109/bibm.2014.6999219

Cited by 150 publications

(98 citation statements)

References 13 publications

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“…Deep learning is garnering significant interest and its application is being investigated within many research domains, such as: healthcare [11], [12]; automotive design [13], [14]; manufacturing [15] and law enforcement [16], [17]. There are also several existing works within the domain of NIDS.…”

Section: Existing Workmentioning

confidence: 99%

A Deep Learning Approach to Network Intrusion Detection

Shone

Ngoc

Phai

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

1,154

481

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Abstract-Network Intrusion Detection Systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed non-symmetric deep auto-encoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in GPU-enabled TensorFlow and evaluated using the benchmark KDD Cup '99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

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Section: Existing Workmentioning

confidence: 99%

A Deep Learning Approach to Network Intrusion Detection

Shone

Ngoc

Phai

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

1,154

481

View full text Add to dashboard Cite

show abstract

“…Therefore, with the capability of deep learning to predict the occurrence of diseases accurately, predictive analysis of the future likelihood of diseases has experienced significant progress. Particular techniques that are used for predictive analysis of diseases are autoencoders [96], recurrent neural networks [97] and CNNs [97,98]. On the other hand, it is worth mentioning that in order to improve the accuracy of prediction, sensory data monitoring medical phenomena have to be coupled with sensory data monitoring human behaviour.…”

Section: Opportunities In Smart Health Applications For Deep Learningmentioning

confidence: 99%

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Obinikpo

Kantarcı

2017

JSAN

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Abstract:With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category.

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“…There have been recent developments in bioinformatics that use DBNs for medical text classification [27] and healthcare decision making with electronic medical records [17]. As yet, none have applied deep learning to clinical trial datasets.…”

Section: Related Researchmentioning

confidence: 99%

A Configurable Deep Network for high-dimensional clinical trial data

Donoghue

Roantree

Boxtel

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Clinical studies provide interesting case studies for data mining researchers, given the often high degree of dimensionality and long term nature of these studies. In areas such as dementia, accurate predictions from data scientists provide vital input into the understanding of how certain features (representing lifestyle) can predict outcomes such as dementia. Most research involved has used traditional or shallow data mining approaches which have been shown to offer varying degrees of accuracy in datasets with high dimensionality. In this research, we explore the use of deep learning architectures, as they have been shown to have high predictive capabilities in image and audio datasets. The purpose of our research is to build a framework which allows easy reconfiguration for the performance of experiments across a number of deep learning approaches. In this paper, we present our framework for a configurable deep learning machine and our evaluation and analysis of two shallow approaches: regression and multi-layer perceptron, as a platform to a deep belief network, and using a dataset created over the course of 12 years by researchers in the area of dementia.

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Deep learning for healthcare decision making with EMRs

Cited by 150 publications

References 13 publications

A Deep Learning Approach to Network Intrusion Detection

A Deep Learning Approach to Network Intrusion Detection

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

A Configurable Deep Network for high-dimensional clinical trial data

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