# Using Search Engine Data for Detection and Early Intervention in Suicide Prevention

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $899,041

## Abstract

ABSTRACT. Decades of research to improve the prevention and early detection of suicide risk has largely
resulted in the detection of who is most likely to consider suicide, but not when or if that is most likely to
happen. Most detection methods presume patients are in contact with the healthcare system, which only
reaches a proportion of the at-risk population. Many people at high risk for suicide do not seek professional
help because of lack of time, stigma, and fear regarding how they will be treated in the health care system. It is
imperative that we develop methods that can identify proximal risk for suicide that does not depend on system-
level contact. Web-based search tools are ubiquitous, with 46% of the global population using the internet for
information searches and 1.2 trillion searches per year worldwide. Based on our preliminary data, we propose
that this online search-engine behavior may prove to be an effective, private, and immediate method of
proximal risk detection of suicide for anyone, regardless of their contact with systems of care. We will recruit
1,000 people with mental illness with varying risk for suicide. Participants will provide us access to Google
Take-Out (GTO) data, which includes search-engine history and behavior including YouTube. Participants will
include those who have report a suicide attempt in the past year (N=500), those who have made an attempt
over a year ago (N=250), and those who have thoughts of suicide but never attempted (N=250). All will
participate using gold-standard suicide behavior research instruments. Using a case-crossover design, we will
evaluate the intermittent exposures (search based proximal risk factors) with an immediate and transient effect
on risk and an abrupt outcome (suicide attempt). The case-crossover design is a well-tested and proven
approach especially in cases where transient events can trigger acute events such as cardiovascular events,
injuries, and death due to environmental exposures and has been studied with interview data to determine
warning signs for suicide attempts. Further for predicting suicidal attempt/s, we will use robust ensemble-based
machine learning methods such as random forest, gradient boosting to evaluate the predictive nature of
qualitative and quantitative features. The study will conclude in a collaborative dissemination planning process
with our community partners. Thus, this retrospective and prospective study that aligns GTO data with
carefully assessed suicidal thoughts and behaviors has the potential to identify warning signs in search and
YouTube data that predict when suicidal risk and lay the groundwork for innovative pathways to suicide
prevention.

## Key facts

- **NIH application ID:** 10401836
- **Project number:** 5R01MH123484-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Patricia A. Arean
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $899,041
- **Award type:** 5
- **Project period:** 2021-05-05 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401836

## Citation

> US National Institutes of Health, RePORTER application 10401836, Using Search Engine Data for Detection and Early Intervention in Suicide Prevention (5R01MH123484-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10401836. Licensed CC0.

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