Interpretable Disease Prediction from Clinical Text by Leveraging Pattern Disentanglement-Reference-Cited by-同舟云学术

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Interpretable Disease Prediction from Clinical Text by Leveraging Pattern Disentanglement

Published:2023-10-15 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
language:
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Author:

Ehghaghi Malikeh¹,Zhou Pei-Yuan²,Cheng Wendy Yusi¹,Rajabi Sahar³,Kuo Chih-Hao¹,Lee En-Shiun Annie⁴

Affiliation:

1. University of Toronto,Computer Science Department,Toronto,ON,Canada

2. University of Waterloo,System Design Engineering,Waterloo,ON,Canada

3. University of Tehran,Electrical and Computer Engineering Department,Tehran,Iran

4. Ontario Tech University University of Toronto,Computer Science Department,Toronto,ON,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10313339/10313352/10313408.pdf?arnumber=10313408

Reference33 articles.

1. Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records

2. A probabilistic topic model for clinical risk stratification from electronic health records

3. Topic modeling based classification of clinical reports;kayi;51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop,2013

4. Text classification method based on self-training and LDA topic models

5. Exploring the Space of Topic Coherence Measures

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