# Automated Detection of Suicide Attempt Risk among Bereaved Individuals

> **NIH NIH R21** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $248,112

## Abstract

Project Summary
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.

## Key facts

- **NIH application ID:** 9885577
- **Project number:** 1R21MH121886-01
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Paul K Maciejewski
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $248,112
- **Award type:** 1
- **Project period:** 2020-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9885577

## Citation

> US National Institutes of Health, RePORTER application 9885577, Automated Detection of Suicide Attempt Risk among Bereaved Individuals (1R21MH121886-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9885577. Licensed CC0.

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