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Diagnostic and Statistical Manual of Mental Disorders (DSM)

Diagnostic and Statistical Manual of Mental Disorders (DSM)

The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...

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Updated: Sep 21, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability

Howard H Goldman¹, Julia Porcino¹, Guy Divita¹

¹Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland (all authors); Department of Psychiatry, University of Maryland School of Medicine, Baltimore (Goldman); Department of Computer Science, Johns Hopkins University, Baltimore (Zirikly); Department of Occupational Therapy, Tufts University, Medford, Massachusetts (Marfeo); Department of Physical Therapy, University of Pittsburgh, Pittsburgh (McDonough).

Psychiatric Services (Washington, D.C.)

|June 2, 2022

Summary

This summary is machine-generated.

The Social Security Administration (SSA) is improving mental disability assessments using data science. Informatics research, including natural language processing, aids in reviewing claimant files for better work-related functioning evaluations.

Keywords:

Computer technology Disability evaluation Mental health functioning Social Security

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Last Updated: Sep 21, 2025

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Area of Science:

Informatics
Data Science
Public Health

Background:

The Social Security Administration's (SSA) disability determination for mental impairments is complex.
Assessments rely on extensive documentation, including medical evidence and varied self-reports.
Challenges exist in efficiently and accurately evaluating work-related functioning.

Purpose of the Study:

To enhance the SSA's disability decision-making process for mental impairments.
To develop advanced decision support tools utilizing data science and informatics.
To improve the review of claimant files for mental health functioning information.

Main Methods:

An interagency agreement between SSA and the National Institutes of Health (NIH).
Application of data science and informatics principles.
Development of the Work Disability-Functional Assessment Battery.
Initiation of natural language processing (NLP) for file review.

Main Results:

Development of the Work Disability-Functional Assessment Battery.
Implementation of an NLP approach for analyzing claimant files.
Establishment of a decade-long informatics research collaboration.

Conclusions:

The informatics research collaboration shows promise for improving disability determinations.
NLP and data science tools can aid in evaluating mental health functioning.
Enhanced tools can lead to more efficient and accurate SSA disability decisions.