24 citations · 28 across the 4 of their papers we have counts for
4 papers
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing
Le Peng, Gaoxiang Luo, sicheng zhou +4
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data ac…
A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records
Sicheng Zhou, Nan Wang, Liwei Wang +4
Objective: The generalizability of clinical large language models is usually ignored during the model development process. This study evaluated the generalizability of BERT-based c…
CancerBERT: a BERT model for Extracting Breast Cancer Phenotypes from Electronic Health Records
Sicheng Zhou, Liwei Wang, Nan Wang +2
Accurate extraction of breast cancer patients' phenotypes is important for clinical decision support and clinical research. Current models do not take full advantage of cancer doma…
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text
Yadan Fan, Sicheng Zhou, Yifan Li +1
The objective of our work is to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DS) in clinical t…