Hong Yu Image by Ed Brennen
CHORDS Director Hong Yu is the lead author of the research paper published in "JAMA Network Open."

By Brooke Coupal

Members of the Center of Biomedical and Health Research in Data Sciences (CHORDS) want to improve suicide prevention.

“Suicide has touched everyone’s lives,” says CHORDS Director Hong Yu, a Miner School of Computer and Information Sciences professor. “I believe our work has the potential to prevent deaths by suicide.”

On average, 121 adults in the United States died by suicide every day in 2020, with veterans having a 57.3% higher suicide rate than non-veteran adults, according to a U.S. Department of Veterans Affairs (VA) report published in September 2022.

CHORDS researchers, including Yu, Biological Sciences Asst. Prof. Rachel Melamed, Biomedical and Nutritional Sciences Prof. Katherine Tucker and Weisong Liu, a Miner School research assistant professor and CHORDS assistant director, examined the link between suicide risk among veterans and social determinants of health, such as housing instability, financial problems and violence. The research was funded by National Institute of Mental Health grants totaling more than $3 million.

Their findings, recently published in “JAMA Network Open,” showed how natural language processing can be used to analyze all available information about social determinants of health, leading to better suicide risk assessment and prevention.

Critical Data Often Overlooked

When investigating causes of suicidal behavior, Hong says researchers often turn to structured data from electronic health records, which include billing and disease codes. The problem with focusing on structured data is that it often lacks contextual information about the patients, including the social determinants of health.

“Social determinants of health are severely undercoded,” says Yu, who adds that this information is typically found in unstructured data, such as clinical notes from physicians, social workers and other providers.

With the lack of a comprehensive database for that type of information, Yu and her team sought a way to gather unstructured data to better analyze the association between suicide and social determinants of health.

The researchers developed a natural language processing system that could extract social determinants of health from unstructured clinical notes found in the U.S. Veterans Health Administration’s electronic health records, making it the first large-scale case study to ever do so. 

From there, Richeek Pradhan, a co-author of the paper and Yu’s former graduate student at the UMass Chan Medical School, helped create a nested case-control study to compare data between veterans who had died by suicide with those who had not.

Using the unstructured data findings from the natural language processing system and the already available structured data, the researchers found that social determinants of health are linked with an increased risk of suicide, with the strongest association coming from legal problems and violence. 

By using natural language processing, hospitals can better identify patients at risk of suicide based on their social determinants of health, the researchers say. Providers can then point patients to the appropriate services where they can get help, whether that is lawyers to assist with legal problems, food pantries for those facing food insecurity or shelters to help with housing instability.

“To reduce suicide, we need to reduce social determinants of health,” Melamed says.

“For all of the social determinants of health that we focused on, there are specific social services that can help,” Yu adds. “Our goal is to improve the quality of care for patients and try to improve their outcomes.”

Yu is leading several other multimillion dollar projects within CHORDS, including one recently funded by a $4 million National Institute of Aging grant that looks at social determinants of health and Alzheimer’s disease among veterans and another to be funded by a more than $1 million VA Health Services Research and Development grant aimed at developing technologies to help prevent opioid overdoses.

“I want to keep building CHORDS and make it a research powerhouse,” Yu says.