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United States Department of Veterans Affairs

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Comparison of Prediction Model Performance Updating Protocols: Using a Data-Driven Testing Procedure to Guide Updating.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In evolving clinical environments, the accuracy of prediction models deteriorates over time. Guidance on the design of model updating policies is limited, and there is limited exploration of the impact of different policies on future model performanc...

Regional Variations in Documentation of Sexual Trauma Concepts in Electronic Medical Records in the United States Veterans Health Administration.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Experiences of sexual trauma are associated with adverse patient and health system outcomes, but are not systematically documented in electronic health records (EHR). To describe variations in how sexual trauma is documented in the Veterans Health ...

Measuring Exposure to Incarceration Using the Electronic Health Record.

Medical care
BACKGROUND: Electronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract.

Does machine learning improve prediction of VA primary care reliance?

The American journal of managed care
OBJECTIVES: The Veterans Affairs (VA) Health Care System is among the largest integrated health systems in the United States. Many VA enrollees are dual users of Medicare, and little research has examined methods to most accurately predict which vete...

Improvements to PTSD quality metrics with natural language processing.

Journal of evaluation in clinical practice
RATIONALE AIMS AND OBJECTIVES: As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These t...

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.

BMJ health & care informatics
Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 20...

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

ESC heart failure
AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes....

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Psychiatry research
Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to anal...

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

JCO clinical cancer informatics
PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documenta...

Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.

Journal of psychiatric research
Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suic...