101 citations · 193 across the 10 of their papers we have counts for
10 papers
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
Cheng Peng, Xi Yang, Zehao Yu +3
Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehens…
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
Shantanu Ghosh, Zheng Feng, Jiang Bian +2
Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is chal…
Clinical Relation Extraction Using Transformer-based Models
Xi Yang, Zehao Yu, Yi Guo +2
The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performance…
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Weishen Pan, Sen Cui, Jiang Bian +2
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the devel…
A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models
Zehao Yu, Xi Yang, Chong Dang +8
Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, fa…
Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions
Mattia Prosperi, Simone Marini, Christina Boucher +1
Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information…
Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter
Yefeng Wang, Yunpeng Zhao, Jiang Bian +1
Objective: The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter. Material and Met…
A Conversational Agent System for Dietary Supplements Use
Esha Singh, Anu Bompelli, Ruyuan Wan +3
Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consume…
Applications of artificial intelligence in drug development using real-world data
Zhaoyi Chen, Xiong Liu, William Hogan +2
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflectin…
Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods
Xi Yang, Yan Gong, Nida Waheed +4
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for car…