Suicide continues to be among the top 10 leading causes of death in the US. Despite decades of research, our ability to predict when someone might be at the highest risk of self-harm suicide has not significantly improved. Personalized social media and online search history data, by contrast, could provide an ongoing real-world datastream revealing internal thoughts and personal states of mind.

1. Abhi Pratap, Ph.D.
Group Head | Digital Health & AI
Independent Scientist, CAMH
Assistant Professor, University of Toronto
Learning Real-World Individualized Risk Factors of
Suicide
Patricia A Areán, Honor Hsin, Tierney K Huppert, Patrick J Heagerty, Trevor Cohen, Courtney Bagge, Katherine Anne Comtois

2. Suicide is the
10
leading
cause of death
in the US
Each year
40,000+
American’s die
due to Suicide
Suicide
costs the US
$50+billion
Annually
https://afsp.org/about-suicide/suicide-statistics/
120+
precious lives lost
in the US everyday

3. Over 50+ years of research has shown limited success
in predicting risk of suicide
We know WHO is at risk but NOT WHEN
they maybe at a higher risk of self harm
5 broad categories account for nearly
80% of all risk factors

5. Areán PA*, Pratap A*, Hsin H, Huppert TK, Hendricks KE, Heagerty PJ, Cohen T, Bagge C, Comtois
KA Perceived Utility and Characterization of Personal Google Search Histories to Detect Data Patterns
Proximal to a Suicide Attempt in Individuals Who Previously Attempted Suicide: Pilot Cohort Stud
y
J Med Internet Res 2021;23(5):e27918
Learn real-world risk factors of self-
harm from individualized online
information seeking behavior
1. Data Featurization
2. Study Design
3. Results
4. Next Steps

6. 1. Data featurization - Human-augmented
Matching search queries to known warning signs of self-harm
Anger
Anxiety
Suicide
Methods
… … …
1. Expert curated
s
e
r
a
c
h
q
u
e
r
y
Trevor Cohen et. al. - UW
Alcohol
Individualized behavior pattern proximal to self-harm
Polarity detection
using deep-learning based context-aware Distil BERT model
Temporal changes in online searches
using context-aware Sentence BERT model
2. Data-driven
Amir et. al. - AID4MH

7. 2. Study design - Case crossover

8. Baseline online search behavior
of a speci
fi
c individual
Redline shows online
search behavior
7-60 days before a
con
fi
rmed suicide attempt
3. Results
Num Daily Searches Num Daily Searches Searches related to suicide methods
Searches related to anxiety Searches related to suicide communication

9. Next Steps - A larger study to assess real-world risk
factors across the spectrum of self-harm

10. • Compare risk factors across the risk spectrum
• Assess shared patterns of risks
• Transparent data analysis pipeline
• Viability of clinical deployment
• Collection of additional novel digital end-points
(eg. Continual physiological assessment)

11. Search history
EHR
Search patterns, circadian patterns, rhythms, language semantics, frequency, one o
ff
vs patterns
Location history
Mobility, geospatial contextually
clinic visit
Missed appointments
You Tube
EMA
Passive Monitoring
Smartphones
Wearables
Generate condition speci
fi
c
real-world evidence
(socio-environmental sensing)
U
l
t
i
m
a
t
e
l
y
Triangulate novel sources of real-world data that matter to people

12. @abhishekpratap
abhishek.pratap@camh.ca
Hiring
www.aid4mental.health/positions
Thank You