Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2007 Apr;48(2):185-97.

Posttraumatic stress disorder: diagnostic data analysis by data mining methodology

Affiliations
Free PMC article

Posttraumatic stress disorder: diagnostic data analysis by data mining methodology

Igor Marinić et al. Croat Med J. 2007 Apr.
Free PMC article

Abstract

Aim: To use data mining methods in assessing diagnostic symptoms in posttraumatic stress disorder (PTSD). METHODS. The study included 102 inpatients: 51 with a diagnosis of PTSD and 51 with psychiatric diagnoses other than PTSD. Several models for predicting diagnosis were built using the random forest classifier, one of the intelligent data analysis methods. The first prediction model was based on a structured psychiatric interview, the second on psychiatric scales (Clinician-administered PTSD Scale--CAPS, Positive and Negative Syndrome Scale--PANSS, Hamilton Anxiety Scale--HAMA, and Hamilton Depression Scale--HAMD), and the third on combined data from both sources. Additional models placing more weight on one of the classes (PTSD or non-PTSD) were trained, and prototypes representing subgroups in the classes constructed.

Results: The first model was the most relevant for distinguishing PTSD diagnosis from comorbid diagnoses such as neurotic, stress-related, and somatoform disorders. The second model pointed out the scores obtained on the CAPS scale and additional PANSS scales, together with comorbid diagnoses of neurotic, stress-related, and somatoform disorders as most relevant. In the third model, psychiatric scales and the same group of comorbid diagnoses were found to be most relevant. Specialized models placing more weight on either the PTSD or non-PTSD class were able to better predict their targeted diagnoses at some expense of overall accuracy. Class subgroup prototypes mainly differed in values achieved on psychiatric scales and frequency of comorbid diagnoses.

Conclusion: Our work demonstrated the applicability of data mining methods for the analysis of structured psychiatric data for PTSD. In all models, the group of comorbid diagnoses, including neurotic, stress-related, and somatoform disorders, surfaced as important. The important attributes of the data, based on the structured psychiatric interview, were the current symptoms and conditions such as presence and degree of disability, hospitalizations, and duration of military service during the war, while CAPS total scores, symptoms of increased arousal, and PANSS additional criteria scores were indicated as relevant from the psychiatric symptom scales.

PubMed Disclaimer

References

    1. Sadock BJ, Sadock VA. Kaplan and Sadock's concise textbook of clinical psychiatry. 2nd ed. Baltimore (MD): Williams & Wilkins; 2004.
    1. Kozarić-Kovačić D, Marušić A, Ljubin T. Combat-experienced soldiers and tortured prisoners of war differ in the clinical presentation of PTSD. Nord J Psychiatry. 1999;53:11–5.
    1. Kozaric-Kovacic D, Kocijan-Hercigonja D. Assessment of post-traumatic stress disorder and comorbidity. Mil Med. 2001;166:677–80. - PubMed
    1. Kaplan HI, Sadock BJ, Grebb JA. Kaplan and Sadock’s Synopsis of Psychiatry. 7th ed. Baltimore (MD): Williams & Wilkins; 1994.
    1. Kozaric-Kovacic D, Bajs M, Vidosic S, Matic A, Alegic Karin A, Peraica T. Change of diagnosis of post-traumatic stress disorder related to compensation-seeking. Croat Med J. 2004;45:427–33. - PubMed

Publication types