TY - GEN
T1 - Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
AU - Yao, Liang
AU - Mao, Chengsheng
AU - Luo, Yuan
N1 - Funding Information:
ACKNOWLEDGMENT We would like to thank i2b2 National Center for Biomedical Computing funded by U54LM008748, for providing the clinical records originally prepared for the Shared Tasks for Challenges in NLP for Clinical Data organized by Dr. Ozlem Uzuner. We thank Dr. Uzuner for helpful discussions. We would like to also thank NVIDIA GPU Grant program for providing the GPU used in our computation. This work was supported in part by NIH Grant 1R21LM012618-01.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/16
Y1 - 2018/7/16
N2 - 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.
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.
KW - clinical text classification, obesity challenge, convolutional neural networks, word embeddings, entity embeddings
UR - http://www.scopus.com/inward/record.url?scp=85051017429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051017429&partnerID=8YFLogxK
U2 - 10.1109/ICHI-W.2018.00024
DO - 10.1109/ICHI-W.2018.00024
M3 - Conference contribution
AN - SCOPUS:85051017429
T3 - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
SP - 70
EP - 71
BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Y2 - 4 June 2018 through 7 June 2018
ER -
