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Natural language processing system for rapid detection and intervention of mental health crisis chat messages

Medicine and Health

Natural language processing system for rapid detection and intervention of mental health crisis chat messages

A. Swaminathan, I. López, et al.

Discover how Crisis Message Detector-1 (CMD-1), developed by a remarkable team including Akshay Swaminathan and Jonathan H. Chen, revolutionizes mental health crisis communication. This advanced NLP system dramatically reduces response times and enhances triage accuracy in telehealth services, ensuring that urgent messages are swiftly identified and managed.

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Playback language: English
Abstract
This paper details the development and deployment of Crisis Message Detector-1 (CMD-1), a machine learning-enabled natural language processing (NLP) system designed to expedite the triage of mental health crisis chat messages within a large telehealth network. The two-stage NLP system, incorporating keyword filtering and logistic regression, was trained on 721 chat messages (32% representing potential crises). Retrospective (N=481) and prospective (N=102,471) test sets demonstrated high sensitivity (0.99 and 0.98, respectively) and improved AUC (0.82 and 0.98, respectively) and PPV (0.35 and 0.66, respectively). CMD-1 significantly reduced median response times from 9 hours to 8-13 minutes. The study concludes that NLP-based models can effectively identify crisis messages, aiding human triage within existing clinical workflows.
Publisher
npj Digital Medicine
Published On
Nov 21, 2023
Authors
Akshay Swaminathan, Iván López, Rafael Antonio Garcia Mar, Tyler Heist, Tom McClintock, Kaitlin Caoili, Madeline Grace, Matthew Rubashkin, Michael N. Boggs, Jonathan H. Chen, Olivier Gevaert, David Mou, Matthew K. Nock
Tags
Crisis Message Detector
natural language processing
mental health
telehealth
machine learning
NLP system
triage
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