Journals Information
Computer Science and Information Technology Vol. 7(4), pp. 111 - 127
DOI: 10.13189/csit.2019.070402
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Deep CNN with Residual Connections and Range Normalization for Clinical Text Classification
Jonah. K. Kenei 1,*, Juliet. C. Moso 2, Elisha T. Opiyo Omullo 1, Robert Oboko 1
1 School of Computing and Informatics, University of Nairobi, Kenya
2 Department of Computer Science, Dedan Kimathi University of Technology, Kenya
ABSTRACT
Deep learning has achieved remarkable performance in many classification tasks such as image processing and computer vision. Due to its impressive performance, deep learning techniques have found their way into natural language processing tasks as well. Deep learning methods are based on neural network architectures such as CNN (Convolutional Neural Networks) with many layers. Deep learning methods have shown state of-the-art performance on many classification tasks through several research works. It has shown great promise in many NLP (Natural language processing) tasks such as learning text representations. In this paper, we study the possibility of using deep learning methods and techniques in clinical documents classification. We review various deep learning-based techniques and their applications in classifying clinical documents. Further, we identify research challenges and describe our proposed convolutional neural network with residual connections and range normalization. Our proposed model automatically learns and classifies clinical sentences into multi-faceted clinical classes, which can help physicians to navigate patients' medical histories easily. Our propose technique uses sentence embedding and Convolutional Neural Network with residual connections and range normalization. To the best of our knowledge, this is the first time that sentence embedding and deep convolutional neural networks with residual connections and range normalization have been simultaneously applied to text processing. Lastly, this work follows a generalized conclusion on clinical documents classification and references.
KEYWORDS
Text Classification, Document Classification, Unstructured Text, Deep Learning, Word Embeddings, Sentence Embeddings, Convolutional Neural Network
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Jonah. K. Kenei , Juliet. C. Moso , Elisha T. Opiyo Omullo , Robert Oboko , "Deep CNN with Residual Connections and Range Normalization for Clinical Text Classification," Computer Science and Information Technology, Vol. 7, No. 4, pp. 111 - 127, 2019. DOI: 10.13189/csit.2019.070402.
(b). APA Format:
Jonah. K. Kenei , Juliet. C. Moso , Elisha T. Opiyo Omullo , Robert Oboko (2019). Deep CNN with Residual Connections and Range Normalization for Clinical Text Classification. Computer Science and Information Technology, 7(4), 111 - 127. DOI: 10.13189/csit.2019.070402.