Recurrent neural networks for classifying relations in clinical notes.
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TLDR
The first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes show comparable performance to previously published systems while requiring no manual feature engineering.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2017-08-01 and is currently open access. It has received 137 citations till now. The article focuses on the topics: Word embedding & Feature engineering.read more
Citations
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Bidirectional LSTM with attention mechanism and convolutional layer for text classification
Gang Liu,Jiabao Guo +1 more
TL;DR: A novel and unified architecture which contains a bidirectional LSTM (BiLSTM), attention mechanism and the convolutional layer is proposed in this paper, which outperforms other state-of-the-art text classification methods in terms of the classification accuracy.
Journal ArticleDOI
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.
Cao Xiao,Edward Choi,Jimeng Sun +2 more
TL;DR: A systematic review of deep learning models for electronic health record (EHR) data is conducted, and various deep learning architectures for analyzing different data sources and their target applications are illustrated.
Posted Content
Graph Convolutional Networks for Text Classification
TL;DR: Zhang et al. as mentioned in this paper proposed a Text Graph Convolutional Network (Text GCN) for text classification, which jointly learns the embeddings for both words and documents, as supervised by the known class labels for documents.
Journal ArticleDOI
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
TL;DR: In this article, the authors used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to the disease from public opinions.
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A clinical text classification paradigm using weak supervision and deep representation.
Yanshan Wang,Sunghwan Sohn,Sijia Liu,Feichen Shen,Liwei Wang,Elizabeth J. Atkinson,Shreyasee Amin,Hongfang Liu +7 more
TL;DR: In this article, a clinical text classification paradigm using weak supervision and deep representation was proposed to reduce human efforts of labeled training data creation and feature engineering for applying machine learning to clinical text classi cation.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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Learning long-term dependencies with gradient descent is difficult
TL;DR: This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.