Evidence map›Paper›PMID 42350774›Full record

ArticleNPJ digital medicine2026

Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents.

Paul Alexander Bloom, Isaac N Treves, David Pagliaccio, Isabella Nadel, Emma Wool, Hayley Quinones, Julia Greenblatt, Natalia Parjane, Katherine Durham, Samantha Salem and 24 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

34 authors.

Paul Alexander Bloom *Department of Psychiatry, Columbia University, New York, NY, USA. paul.bloom@nyspi.columbia.edu.
Isaac N Treves *Department of Psychiatry, Columbia University, New York, NY, USA.
David PagliaccioDepartment of Psychiatry, Columbia University, New York, NY, USA.
Isabella NadelDepartment of Psychiatry, Columbia University, New York, NY, USA.
Emma WoolDepartment of Psychiatry, Columbia University, New York, NY, USA.
Hayley QuinonesWashington University School of Medicine, Washington University, St. Louis, MO, USA.
Julia GreenblattDepartment of Psychiatry, Columbia University, New York, NY, USA.
Natalia ParjaneDepartment of Psychiatry, Columbia University, New York, NY, USA.
Katherine DurhamDepartment of Psychiatry, Columbia University, New York, NY, USA.
Samantha SalemDepartment of Psychiatry, Columbia University, New York, NY, USA.
Esha TrivediDepartment of Psychiatry, Columbia University, New York, NY, USA.
Hanga GalfalvyDepartment of Psychiatry, Columbia University, New York, NY, USA.
Nicholas B AllenDepartment of Psychology, University of Oregon, Eugene, OR, USA.
Deanna M BarchWashington University School of Medicine, Washington University, St. Louis, MO, USA.
Ashley BlanchardDepartment of Emergency Medicine, Columbia University Medical Center, New York, NY, USA.
David BrentDepartment of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Lauren S ChernickDepartment of Emergency Medicine, Columbia University Medical Center, New York, NY, USA.
Peter S DayanDepartment of Emergency Medicine, Columbia University Medical Center, New York, NY, USA.
Caroline HoyniakWashington University School of Medicine, Washington University, St. Louis, MO, USA.
Karla JoyceDepartment of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Jaclyn S KirshenbaumDepartment of Psychiatry, Columbia University, New York, NY, USA.
Lilian Y LiDepartment of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
Joan LubyWashington University School of Medicine, Washington University, St. Louis, MO, USA.
Simryn MolinaDepartment of Psychiatry, Columbia University, New York, NY, USA.
Giovanna PortaWestern Psychiatric Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Zoe PriceDepartment of Psychiatry, Columbia University, New York, NY, USA.
Eva PurvinDepartment of Psychiatry, Columbia University, New York, NY, USA.
Alexander Saul RosenbergDepartment of Psychiatry, Columbia University, New York, NY, USA.
Koustuv SahaUniversity of Illinois Urbana-Champaign, Urbana, IL, USA.
Stewart A ShankmanDepartment of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
Adela SchwartzDepartment of Psychiatry, Columbia University, New York, NY, USA.
Soorya Ram ShimgekarUniversity of Illinois Urbana-Champaign, Urbana, IL, USA.
Jamie ZelaznySchool of Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
Randy P AuerbachDepartment of Psychiatry, Columbia University, New York, NY, USA.

Funding

Social Processing Deficits in Remitted Adolescent DepressionR01MH119771 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI AUERBACH, RANDY PATRICK, SHANKMAN, STEWART AARON · 2020 to 2025
$3.7M
MAPS: Mobile Assessment for the Prediction of SuicideU01MH116923 · NIMH · UNIVERSITY OF OREGON · PI ALLEN, NICHOLAS B, AUERBACH, RANDY PATRICK · 2018 to 2022
$3.6M
NIMH NIH HHS R01 MH119771NIMH NIH HHS R01 MH119771, U01 MH116923NIMH NIH HHS U01 MH116923
6 · The paper itself

Abstract

Adolescent suicide rates have risen over the past two decades, underscoring the need for improved risk detection strategies. Although natural language processing (NLP) tools are increasingly used to flag suicide-related content, little is known about how such approaches perform on adolescents' smartphone communications. Addressing this gap, this study leverages passively collected smartphone data to identify suicide-related language in adolescents' keyboard usage via NLP. We developed a lexicon of suicide-related adolescent language and validated it with labeled data (N = 171,468 text entries; e.g., messages, web searches), demonstrating higher performance in identifying suicide-related text than few-shot prediction with large language models (LLMs) and lexicons not designed for youth. Across two independent cohorts at elevated suicide risk (Ns = 208 & 257; >6 million text entries), lifetime suicidal thoughts and behaviors (STB) and current suicidal ideation were associated with increased frequency of smartphone suicide-related language. Human coding indicated varied language, including authentic first-person current suicidal ideation (14.5%) and jokes or hyperbole (20.2%). Compared with the lexicon alone, human coding of suicide-related entries with first-person language showed stronger associations with STB history. These findings highlight that effective NLP-based tools for suicide prevention will require more nuanced and context-specific approaches to better distinguish suicidal intent.

Identifiers

PMID42350774
PMCPMC13669378

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.