Brian Shiner, MD, MPH

VAMC White River Junction, White River Junction, Vermont; Department of Psychiatry, Geisel School of Medicine, Hanover, New Hampshire; National Center for PTSD, White River Junction, Vermont

  • 9Articles
  • 49.1KReads
  • 2016–2026Active
  • 30Collaborators
  • 134Citations

About

Brian Shiner, MD, MPH, is affiliated with VAMC White River Junction, the Department of Psychiatry at Geisel School of Medicine, and the National Center for PTSD in Vermont. His published work from 2016 to 2026 focuses on posttraumatic stress disorder, suicide risk assessment, veterans' mental health, depression, electroconvulsive therapy, and schizophrenia/schizoaffective disorders. He has contributed to research on natural language processing applications in suicide risk prediction, comparative effectiveness of PTSD medications, and the impact of electroconvulsive therapy on suicidal behavior among veterans.

Publications

JCP
Using Natural Language Processing to Evaluate Differences in Psychotherapeutic Services for PTSD in a Suicide-Risk-Stratified Veteran Sample
Original Research

Using Natural Language Processing to Evaluate Differences in Psychotherapeutic Services for PTSD in a Suicide-Risk-Stratified Veteran Sample

April 8, 2026

Psychotherapeutic care for PTSD differed for patients who did and did not die by suicide, suggesting the importance of developing risk-tier-specific care.
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JCP
Using Natural Language Processing in Suicide Risk Prediction
Original Research

Using Natural Language Processing in Suicide Risk Prediction

June 19, 2023

Natural language processing, a subfield of AI that evaluates textual patterns, added predictive accuracy to the current model of using structured EHR data in suicide risk prediction.
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JCP
Tools to Detect Suicide Risk
Meta-Analysis

Tools to Detect Suicide Risk

November 16, 2022

More than 20 instruments have been developed to assist in detecting suicide risk in adults. This systematic review used likelihood ratio analysis to determine the diagnostic accuracy of instruments...
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JCP
ECT and Death by Suicide
Original Research

ECT and Death by Suicide

April 13, 2022

It is unclear if a course of electroconvulsive therapy (ECT) is associated with a decreased risk of death by suicide. This study examined the association of adequate exposure to...
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JCP
Original Research

ECT and Emergency Department Use in Homeless Veterans

October 26, 2021

Homeless veterans are 4 times as likely to use ED services compared to veterans who are not homeless. This study of VA data looked at the effect of electroconvulsive...
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JCP
Original Research

Medications for DSM-5 PTSD

October 13, 2020

This retrospective study explored whether 5 medications for PTSD with efficacy in meta-analyses of randomized controlled trials—fluoxetine, paroxetine, sertraline, topiramate, and venlafaxine—are also effective in routine clinical practice.
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JCP
Article

Comparing the Effectiveness of Medications for PTSD

September 18, 2018

When choosing among agents for treating PTSD, which one should you pick? Fluoxetine, paroxetine, sertraline, topiramate, and venlafaxine have all shown efficacy. This large retrospective VA study compared the...
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JCP
Original Research

Anticonvulsants in PTSD

May 24, 2017

Anticonvulsants are often prescribed to veterans with PTSD, but how certain are we that these agents are effective for this indication? Read this analysis of medical records from the...
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JCP
Original Research

Paliperidone Palmitate vs. Oral Atypical

October 26, 2016

Read a study comparing oral antipsychotics with paliperidone palmitate in a veteran population to evaluate health care resource utilization with each type of schizophrenia treatment.
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Physicians Postgraduate Press, Inc. (PPP) makes no warranties about the accuracy or completeness of any information published in The Journal of Clinical Psychiatry or other PPP materials, and disclaims liability for any use or non-use of that information. Clinicians should not rely solely on these materials and should exercise their own professional judgment when making patient care decisions on an individualized basis.

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