PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data-Reference-Cited by-同舟云学术

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PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data

Published:2023-06-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)
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Author:

Zhang Shenghan¹,Li Haoxuan²,Tang Ruixiang³,Ding Sirui⁴,Rasmy Laila⁵,Zhi Degui⁵,Zou Na⁶,Hu Xia³

Affiliation:

1. Xi’an Jiaotong-Liverpool University,Department of Computer Science,Xi’An,China

2. Wuhan University,Department of Computer Science,Wuhan,China

3. Rice University,Department of Computer Science,Houston,USA

4. Texas A&M University,Department of Computer Science and Engineering,College Station,USA

5. UTHealth Houston School of Biomedical Informatics,Houston,USA

6. Texas A&M Univerisity,Department of Engineering Technology & Industrial Distribution,College Station,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10336927/10336922/10337197.pdf?arnumber=10337197

Reference50 articles.

1. Does synthetic data generation of llms help clinical text mining?;Tang,2023

2. Spec: A soft prompt-based calibration on mitigating performance variability in clinical notes summarization;Chuang,2023

3. Llm for patient-trial matching: Privacy-aware data augmentation towards better performance and generalizability;Yuan,2023

4. Towards fair patient-trial matching via patient-criterion level fairness constraint;Chang,2023

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