Using recurrent neural network models for early detection of heart failure onset.
TLDR
Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12–18 months.About:
This article is published in Journal of the American Medical Informatics Association.The article was published on 2017-03-01 and is currently open access. It has received 662 citations till now. The article focuses on the topics: Multilayer perceptron & Deep learning.read more
Citations
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A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
Journal ArticleDOI
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
TL;DR: In this paper, the authors survey the current research on applying deep learning to clinical tasks based on EHR data, where they find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification.
Journal ArticleDOI
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomasev,Xavier Glorot,Jack W. Rae,Michal Zielinski,Harry Askham,Andre Saraiva,Anne Mottram,Clemens Meyer,Suman V. Ravuri,Ivan Protsyuk,Alistair Connell,Cian Hughes,Alan Karthikesalingam,Julien Cornebise,Hugh Montgomery,Geraint Rees,Chris Laing,Clifton R. Baker,Kelly S. Peterson,Ruth M. Reeves,Demis Hassabis,Dominic King,Mustafa Suleyman,Trevor Back,Christopher Nielson,Christopher Nielson,Joseph R. Ledsam,Shakir Mohamed +27 more
TL;DR: A deep learning approach that predicts the risk of acute kidney injury and provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests are developed.
Journal ArticleDOI
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
TL;DR: The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition, and categorise the studies into generative, discriminative and hybrid methods.
Journal ArticleDOI
Artificial Intelligence in Cardiology
Kipp W. Johnson,Jessica Torres Soto,Benjamin S. Glicksberg,Khader Shameer,Riccardo Miotto,Mohsin Ali,Euan A. Ashley,Joel T. Dudley,Joel T. Dudley +8 more
TL;DR: This paper reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization, and describes the advent of deep learning and related methods collectively called unsupervised learning, which could be applied to enable precision cardiology and improve patient outcomes.
References
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
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Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
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