Manning CICS Researchers Help Connect Veterans’ PTSD, Unmet Social Needs to Cognitive Impairment and Dementia

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A multi-institution research team recently reported that veterans with both PTSD and unmet social needs appear to have higher rates of dementia or cognitive impairment years later. The study involved 3,817,970 veterans—and, therefore, millions of data points—so the team relied on support from researchers in the Manning College of Information and Computer Sciences (CICS) at UMass Amherst, who contributed to the processing of the copious amounts of data needed for the large-scale study.

The team included Manning CICS adjunct professor Hong Yu, who leads the BioNLP Lab at UMass Amherst and UMass Lowell; doctoral candidate Rumeng Li; Avijit Mitra MS '24, PhD '25, who now works as an applied scientist II at Amazon; and Zonghai Yao MS'21, PhD'26, who now is a research assistant professor at UMass Lowell. 

The team developed a natural language processing (NLP) system. NLP is a subfield of AI focused on helping computers understand and generate human language. For the PTSD study, Mitra applied that same system to detect health-related social needs (HRSNs)—individual social circumstances that can impact health, such as financial problems, housing instability and food insecurity—and investigate potential connections between HRSNs, PTSD, and mild cognitive impairment, Alzheimer's disease and Alzheimer’s-related dementia.

Traditionally, epidemiological research has depended on claims and coded EHR (electronic health records) fields, said Yu.

EHR narratives such as doctor’s visit notes, discharge summaries and history reports, for example, have often been neglected by epidemiologists due to concerns about bias within the documentation, as well as the difficulty in extracting information reliably, especially on a large scale, Yu noted.

That’s where NLP comes in, she added. This work shows that these systems “can yield associations consistent with those obtained from structured data,” she said. “Although this consistency does not eliminate potential bias, it provides evidence supporting the incorporation of narrative-derived information into epidemiologic research.”

This is the first group to report integrating AI into an epidemiological study, Yu said.

“This is an unprecedented era for computer scientists to engage in multidisciplinary studies,” Mitra said. “With the rapid advancement in ML (machine learning) and rise of LLMs (large language models), it opens paths for computer scientists to collaborate with the researchers in other fields, extending from physics and biology to public health and legal workflows with a special focus on privacy, security and accountability.”

The research was published in September 2026 in JAMA Network Open.

This story was originally published by the UMass Amherst Office of News & Media Relations.