Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks

Liang Yao, Chengsheng Mao, Yuan Luo*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. In this study, we propose a novel approach which combines rule-based features and knowledge-guided deep learning techniques for effective disease classification. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results show that our method outperforms the state of the art methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-71
Number of pages2
ISBN (Electronic)9781538667774
DOIs
StatePublished - Jul 16 2018
Event6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Other

Other6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

Fingerprint

Learning
Natural Language Processing
Informatics
Obesity
Neural networks
Rule-based
Text classification
Deep learning
Learning methods
Natural language processing

Keywords

  • clinical text classification, obesity challenge, convolutional neural networks, word embeddings, entity embeddings

ASJC Scopus subject areas

  • Information Systems and Management
  • Health Informatics

Cite this

Yao, L., Mao, C., & Luo, Y. (2018). Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 (pp. 70-71). [8411810] (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI-W.2018.00024
Yao, Liang ; Mao, Chengsheng ; Luo, Yuan. / Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks. Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 70-71 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).
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Yao, L, Mao, C & Luo, Y 2018, Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018., 8411810, Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, Institute of Electrical and Electronics Engineers Inc., pp. 70-71, 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI-W.2018.00024

Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks. / Yao, Liang; Mao, Chengsheng; Luo, Yuan.

Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 70-71 8411810 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. In this study, we propose a novel approach which combines rule-based features and knowledge-guided deep learning techniques for effective disease classification. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results show that our method outperforms the state of the art methods.

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Yao L, Mao C, Luo Y. Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 70-71. 8411810. (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). https://doi.org/10.1109/ICHI-W.2018.00024