Health Informatics

Improving the ability to access, understand, and make use of health information.

Research Projects

Investigating the Information Practices of COVID Long-Haulers
Principal Investigator(s): Beth St. Jean Twanna Hodge Jane Behre J. Nicole Miller
Funder: UMD Impact Award - Pandemic Readiness Initiative: https://research.umd.edu/pri Other
Research Areas: Health Informatics > Information Justice, Human Rights, and Technology Ethics > Library and Information Science
This project investigates the information needs, practices, and experiences of people who have long COVID ("COVID long-haulers") in order to learn more about their COVID-related information needs, the ways in which they have gone about fulfilling these needs, and their information-related experiences. W
Environmental Injustice and Deaths of Despair: Lessons from Montana’s Tribal Lands
Principal Investigator(s):
Research Areas: Health Informatics > Information Justice, Human Rights, and Technology Ethics
The proposed project uses the case of Native American Lands in Montana to investigate the dynamic interactions between environmental change and socio-economic conditions, in order to identify potential pathways whereby environmental hardship may contribute to (and result from) forms of socio-economic distress linked to deaths of despair.
III: Small: Bringing Transparency and Interpretability to Bias Mitigation Approaches in Place-based Mobility-centric Prediction Models for Decision
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.

Staff

Recent News

Collage of photos showcasing the disability community and technology with "MIDA Maryland Initiative for Digital Accessibility" written on top.

News Release: Maryland Initiative for Digital Accessibility (MIDA) Launches

Led by the UMD INFO College, MIDA aims to change technology design to include disability communities as equal partners, proactively bui …
Chalkboard drawing of a gun

Dr. Zubin Jelveh: Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It

Using arrest and victimization records from the Chicago PD, a machine learning model can predict the risk of being shot in the next 18 …