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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 70-71 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538667774 |
| DOIs | |
| State | Published - Jul 16 2018 |
| Event | 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018 |
Publication series
| Name | Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 |
|---|
Other
| Other | 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 |
|---|---|
| Country | United States |
| City | New York |
| Period | 6/4/18 → 6/7/18 |
Fingerprint
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
}
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 proceeding › Conference contribution
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
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
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.
ER -