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Yuqi Si
Researcher at University of Texas Health Science Center at Houston
Publications - 21
Citations - 874
Yuqi Si is an academic researcher from University of Texas Health Science Center at Houston. The author has contributed to research in topics: Deep learning & Conditional random field. The author has an hindex of 10, co-authored 20 publications receiving 457 citations. Previous affiliations of Yuqi Si include Sun Yat-sen University & University of Texas System.
Papers
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Journal ArticleDOI
Deep learning in clinical natural language processing: a methodical review.
Stephen Wu,Kirk Roberts,Surabhi Datta,Jingcheng Du,Zongcheng Ji,Yuqi Si,Sarvesh K. Soni,Qiong Wang,Qiang Wei,Yang Xiang,Bo Zhao,Hua Xu +11 more
TL;DR: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly, but growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community is shown.
Journal ArticleDOI
Enhancing clinical concept extraction with contextual embeddings.
TL;DR: This article explored the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText).
Journal ArticleDOI
Effects of dietary Bacillus licheniformis on growth performance, immunological parameters, intestinal morphology and resistance of juvenile Nile tilapia (Oreochromis niloticus) to challenge infections.
TL;DR: Dietary supplementation of B. licheniformis not only increased the growth, immune response and disease resistance of juvenile tilapia, but also influenced anterior intestinal development and integrity.
Journal ArticleDOI
Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review
Yuqi Si,Jingcheng Du,Zhao Li,Xiaoqian Jiang,Timothy A. Miller,Fei Wang,W. Jim Zheng,Kirk Roberts +7 more
TL;DR: The importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review are shown and advances in patient representation learning techniques will be essential for powering patient-level EHR analyses.
Proceedings Article
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.
Qiang Wei,Zongcheng Ji,Yuqi Si,Jingcheng Du,Jingqi Wang,Firat Tiryaki,Stephen Wu,Cui Tao,Kirk Roberts,Hua Xu +9 more
TL;DR: Two different implementations of the BERT model for clinical RE tasks are developed, showing that tuned LMs outperformed previous state-of-the-art RE systems in two shared tasks, which demonstrates the potential of LM-based methods on the RE task.