Bereaved individuals, especially those who meet criteria for Prolonged Grief Disorder (PGD) and those bereaved by suicide, have substantial risk of suicidal ideation, suicide attempts, and suicide. These vulnerable bereaved subgroups are also less likely to receive informal support or access mental health care, creating a pressing need to detect their suicide risk. Each year, well over a thousand unsolicited bereaved individuals visit our Cornell Center for Research on End-of-Life Care website and complete an online tool to determine if they meet criteria for PGD (over 6,000 completers to date). Compared to community-based bereaved samples, those who complete our online tool disproportionately meet criteria for PGD (e.g., ~30% vs. ~10%) and are suicide bereaved (~10% vs. ~2%). Thus, astoundingly, >35% of those who visit our Center website and complete our online diagnostic tool are at significantly elevated risk for suicidal ideation and/or attempts. To respond to the need to detect suicidal thoughts and behaviors (STBs) among our bereaved website visitors, we propose to develop a web-based tool for the detection of suicide attempt risk. We will leverage our Living Memory Home, an online memorial application residing on our Center website, by enhancing its features to optimize data collection, including data on potential implicit indicators of a bereaved person's suicide attempt risk (Aim #1). Data will be gathered on 100 Living Memory Home users daily for a week, followed by a 1-week, 1- and 6-month post-baseline follow-up assessment. The study will generate ~30 texts/subject in the Living Memory Home's Imagined Dialogues with the deceased, and reflections, dreams, stories, and touchstones in Narrative Notes (~3,000 texts in total from all users). Boot-strapping resampling, natural language processing and machine learning techniques will then be applied to develop machine learning models predicting suicide attempt risk based on bereaved subjects' baseline, 1-week, 1- and 6-month Columbia Suicide Severity Rating Scale scores (Aim #2). Our primary outcome will be the 1-week CSSRS score. We will, thus, develop and then pilot test an automated way to detect bereaved persons'; suicide attempt risk based on their interactions with the Living Memory Home. This is a first step toward development of a safe, accurate, potentially scalable online tool for the detection of suicide attempt risk among bereaved individuals.

Public Health Relevance

(Relevance) Bereaved individuals, and particularly those bereaved by suicide and who meet criteria for Prolonged Grief Disorder, are at substantially elevated risk for suicidal thoughts and attempts. This project, applying machine learning techniques to detect suicidal attempt risk among bereaved individuals using an online bereavement resource, the Living Memory Home, is a first step in the development of a safe and effective online tool for the detection of suicide attempt risk among bereaved individuals.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
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Adult Psychopathology and Disorders of Aging Study Section (APDA)
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Ferrante, Michele
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Weill Medical College of Cornell University
Schools of Medicine
New York
United States
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