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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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Automatic mining of symptom severity from psychiatric evaluation notes.

George Karystianis^1,2, Alejo J Nevado³, Chi-Hun Kim³

¹Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

International Journal of Methods in Psychiatric Research

|December 23, 2017

Summary

Automated methods can extract mental health symptom severity from clinical notes. A rule-based approach achieved 80.1% accuracy, supporting research and decision-making.

Keywords:

classification neural networks psychiatric evaluation records rule-based approach text mining

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Area of Science:

Natural Language Processing (NLP)
Computational Psychiatry
Health Informatics

Background:

Electronic mental health records are increasingly available.
Automated information extraction from free-text is crucial for research and clinical support.
Accurate extraction of symptom severity is a key challenge.

Purpose of the Study:

To explore automated extraction of explicit symptom severity mentions from psychiatric evaluations.
To evaluate different NLP methods for this task using the CEGS N-GRID dataset.

Main Methods:

Developed a knowledge-driven, rule-based method using lexical patterns.
Implemented a machine learning approach utilizing a neural network.
Created a hybrid method combining rule-based and neural network techniques.

Main Results:

The rule-based method achieved the highest performance at 80.1% accuracy.
The machine learning approach reached 73.3% accuracy.
The hybrid method achieved 72.0% accuracy on an unseen evaluation set.

Conclusions:

Automated text mining shows promise for classifying mental health symptom severity from clinical notes.
These methods can support epidemiological research and clinical decision-making.
Further work is needed to enhance accuracy.