Research
Publications
Xiangxing Guo, Tongnian Wang, Yuanxiong Guo, Carolina Vivas-Valencia, Cici Bauer, and Yanmin Gong. State-Specific Explainable Machine Learning for Predicting Premature Dropout in Medication for Opioid Use Disorder. IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2025.
Tongnian Wang, Kai Zhang, Jiannan Cai, Yanmin Gong, Kim-Kwang Raymond Choo, Yuanxiong Guo. Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare. Journal of Healthcare Informatics Research (JHIR), 2024.
Tongnian Wang, Yan Du, Yanmin Gong, Kim-Kwang Raymond Choo, Yuanxiong Guo. Applications of Federated Learning in Mobile Health: Scoping Review. Journal of Medical Internet Research (JMIR), 2023.
Tongnian Wang, Xingmeng Zhao, Anthony Rio. UTSA-NLP at RadSum23: Multi-modal Retrieval-Based Chest X-Ray Report Summarization. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks (BioNLP), 2023.
Xingmeng Zhao, Tongnian Wang, Sheri Osborn, Anthony Rio. BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?. The BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, (CoNLL 2023), 2023.
Tongnian Wang, Yuanxiong Guo, Kim-Kwang Raymond Choo. Enabling Privacy-Preserving Prediction for Length of Stay in ICU-A Multimodal Federated-Learning-based Approach. European Conference on Information Systems (ECIS), 2023.
Under Review & In Preparation
T. Wang et al. Toward Trustworthy and Reliable Length of Stay Prediction in Intensive Care Unit: A Fairness-Aware Personalized Federated Learning Approach.
T. Wang et al. Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder.
T. Wang et al. Harnessing Large Language Models to Predict Treatment Outcomes in Medication for Opioid Use Disorder.
X. Zhao, T. Wang, A. Rios. Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary.