Corpus ID: 11309794

Medical Text Classification using Convolutional Neural Networks

@article{Hughes2017MedicalTC,
  title={Medical Text Classification using Convolutional Neural Networks},
  author={Mark Hughes and Irene Li and S. Kotoulas and T. Suzumura},
  journal={Studies in health technology and informatics},
  year={2017},
  volume={235},
  pages={
          246-250
        }
}
We present an approach to automatically classify clinical text at a sentence level. [...] Key Method We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.Expand
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References

SHOWING 1-10 OF 10 REFERENCES
Convolutional Neural Networks for Sentence Classification
TLDR
The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors. Expand
A Convolutional Neural Network for Modelling Sentences
TLDR
A convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) is described that is adopted for the semantic modelling of sentences and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. Expand
Research and applications: N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit
TLDR
SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. Expand
Accurate Cancer Classification Using Expressions of Very Few Genes
  • Lipo Wang, Feng Chu, Wei Xie
  • Computer Science, Medicine
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • 2007
TLDR
In general, the method can significantly reduce the number of genes required for highly reliable diagnosis. Expand
Distributed Representations of Sentences and Documents
TLDR
Paragraph Vector is an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents, and its construction gives the algorithm the potential to overcome the weaknesses of bag-of-words models. Expand
Automated methods for the summarization of electronic health records
TLDR
This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization with a particular focus on methods for detecting and removing redundancy, describing temporality, determining salience, accounting for missing data, and taking advantage of encoded clinical knowledge. Expand
Redundancy-Aware Topic Modeling for Patient Record Notes
TLDR
A novel variant of Latent Dirichlet Allocation topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes and produces superior models to all three baseline strategies. Expand
Distributed Representations of Words and Phrases and their Compositionality
TLDR
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. Expand
Return of Frustratingly Easy Domain Adaptation
TLDR
This work proposes a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL), which minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Expand
Subspace Distribution Alignment for Unsupervised Domain Adaptation
TLDR
A unified view of existing subspace mapping based methods is presented and a generalized approach that also aligns the distributions as well as the subspace bases is developed that shows improved results over published approaches. Expand