Detection of Youth Suicide Interventions in Clinical Record Text using an Open-Source Language Model.

Juliet B Edgcomb, Alexandra Klomhaus, Joshua Lee, Chrislie G Ponce, Elyse Tascione, Angshuman Saha

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing 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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Juliet B EdgcombUniversity of California, Los Angeles, Los Angeles, California, USA.

Alexandra KlomhausUniversity of California, Los Angeles, Los Angeles, California, USA.

Joshua LeeUniversity of California, Los Angeles, Los Angeles, California, USA.

Chrislie G PonceUniversity of California, Los Angeles, Los Angeles, California, USA.

Elyse TascioneUniversity of California, Los Angeles, Los Angeles, California, USA.

Angshuman SahaUniversity of California, Los Angeles, Los Angeles, California, USA.

Funding

Electronic Health Record Phenotyping for Case Detection and Prediction of Emergency Department Visits for Child and Adolescent Suicide AttemptsK23MH130745 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI EDGCOMB, JULIET BENI · 2022 to 2025

$844k

NIMH NIH HHS K23 MH130745

6 · The paper itself

Abstract

This study presents an automated approach to detect youth suicide prevention interventions documented in emergency department notes. Expert review classified four interventions across 1,794 notes from 200 emergency visits for suicidality among children aged 6-17. The open-source language model Llama3.3-70B generated Likert scores (-3 to +3) for intervention presence. Scores approximated human classification: Lethal Means Restriction (AUROC 0.972, 95%CI:0.961-0.982), Hotline (AUROC 0.980, 95%CI:0.966-0.990), Outpatient Referral (AUROC 0.935, 95%CI:0.922-0.947), Safety Planning (AUROC 0.954, 95%CI:0.930-0.975). Application to 6,687 notes from 723 encounters revealed increased odds of omitted interventions among youth with prior emergency visits (OR 0.454-0.625), missing screening questions (OR 0.169-0.653), and ideation (vs. acts or attempts) (OR 0.296, 95%CI 0.133-0.657). Findings demonstrate systematic clinical text analysis with an open-source language model can expose new targets to inform decision support and strengthen evidence-based care for youth suicide.

Indexed as

Electronic Health RecordsNatural Language ProcessingSuicide PreventionAdolescentChildEmergency Service, HospitalHumansLarge Language Models

Identifiers

PMID41726432

PMCPMC12919508

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.