5 papers
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
Tongnian Wang, Carolina Vivas-Valencia, Cici Bauer +3
Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predict retention and identify pa…
Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI
Xingmeng Zhao, Tongnian Wang, Dan Schumacher +2
Artificial intelligence (AI) is rapidly transforming healthcare, enabling fast development of tools like stress monitors, wellness trackers, and mental health chatbots. However, ra…
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
Zhidong Gao, Zhenxiao Zhang, Yu Zhang +3
Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been d…
Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary
Xingmeng Zhao, Tongnian Wang, Anthony Rios
Radiology report summarization (RRS) is crucial for patient care, requiring concise "Impressions" from detailed "Findings." This paper introduces a novel prompting strategy to enha…
BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?
Xingmeng Zhao, Tongnian Wang, Sheri Osborn +1
Language models have seen significant growth in the size of their corpus, leading to notable performance improvements. Yet, there has been limited progress in developing models tha…