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Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models

Published online by Cambridge University Press:  17 February 2020

Maxwell Levis Open the ORCID record for Maxwell Levis [Opens in a new window] ,
Christine Leonard Westgate ,
Jiang Gui ,
Bradley V. Watts  and
Brian Shiner
Maxwell Levis*
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA Geisel School of Medicine at Dartmouth, Hanover, NH, USA
Christine Leonard Westgate
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA
Jiang Gui
Affiliation:
Geisel School of Medicine at Dartmouth, Hanover, NH, USA
Bradley V. Watts
Affiliation:
Geisel School of Medicine at Dartmouth, Hanover, NH, USA VA Office of Systems Redesign and Improvement, White River Junction, VT, USA
Brian Shiner
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA Geisel School of Medicine at Dartmouth, Hanover, NH, USA VA Office of Systems Redesign and Improvement, White River Junction, VT, USA National Center for PTSD Executive Division, White River Junction, VT, USA
*
Author for correspondence: Maxwell Levis, E-mail: [email protected]
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Abstract

Background

This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.

Methods

We used a cohort of Veterans Health Administration (VHA) users diagnosed with post-traumatic stress disorder (PTSD) between 2004–2013. Using a case-control design, cases (those that died by suicide during the year following diagnosis) were matched to controls (those that remained alive). After selecting conditional matches based on having shared mental health providers, we chose controls using a 5:1 nearest-neighbor propensity match based on the VHA's structured Electronic Medical Records (EMR)-based suicide prediction model. For cases, psychotherapist notes were collected from diagnosis until death. For controls, psychotherapist notes were collected from diagnosis until matched case's date of death. After ensuring similar numbers of notes, the final sample included 246 cases and 986 controls. Notes were analyzed using Sentiment Analysis and Cognition Engine, a Python-based NLP package. The output was evaluated using machine-learning algorithms. The area under the curve (AUC) was calculated to determine models' predictive accuracy.

Results

NLP derived variables offered small but significant predictive improvement (AUC = 0.58) for patients that had longer treatment duration. A small sample size limited predictive accuracy.

Conclusions

Study identifies a novel method for measuring suicide risk over time and potentially categorizing patient subgroups with distinct risk sensitivities. Findings suggest leveraging NLP derived variables from psychotherapy notes offers an additional predictive value over and above the VHA's state-of-the-art structured EMR-based suicide prediction model. Replication with a larger non-PTSD specific sample is required.

Keywords

Electronic medical recordsnatural language processingsuicide predictionveterans mental health
Type
Original Articles
Information
Psychological Medicine , Volume 51 , Issue 8 , June 2021 , pp. 1382 - 1391
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Creative Commons
This is a work of the U.S. Government and is not subject to copyright protection in the United States.
Copyright
Copyright © The Author(s) 2020

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