Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants

TL;DR

This study explores how language models can assist in suicide prevention research by improving data annotation efficiency and accuracy in sensitive public health datasets, potentially leading to better intervention strategies.

Contribution

It introduces a human-in-the-loop algorithm leveraging language models to streamline and enhance the annotation process for suicide-related data variables.

Findings

LM predictions match 85% of existing annotations

LM assists reveal 38% of annotation discrepancies

Algorithm achieves comparable quality with manual annotation

Abstract

Warning: This paper discusses topics of suicide and suicidal ideation, which may be distressing to some readers. The National Violent Death Reporting System (NVDRS) documents information about suicides in the United States, including free text narratives (e.g., circumstances surrounding a suicide). In a demanding public health data pipeline, annotators manually extract structured information from death investigation records following extensive guidelines developed painstakingly by experts. In this work, we facilitate data-driven insights from the NVDRS data to support the development of novel suicide interventions by investigating the value of language models (LMs) as efficient assistants to these (a) data annotators and (b) experts. We find that LM predictions match existing data annotations about 85% of the time across 50 NVDRS variables. In the cases where the LM disagrees with existing annotations, expert review reveals that LM assistants can surface annotation discrepancies 38% of the time. Finally, we introduce a human-in-the-loop algorithm to assist experts in efficiently building and refining guidelines for annotating new variables by allowing them to focus only on providing feedback for incorrect LM predictions. We apply our algorithm to a real-world case study for a new variable that characterizes victim interactions with lawyers and demonstrate that it achieves comparable annotation quality with a laborious manual approach. Our findings provide evidence that LMs can serve as effective assistants to public health researchers who handle sensitive data in high-stakes scenarios.