Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining
Open accessAbstract
Patients’ narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms—including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model—were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners’ diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients’ self-expression behavior, thus helping clinicians identify potential patients from an early stage.
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Some studies used suboptimal tools such as LIWC for POS tagging instead of more suitable alternatives like the spaCy or Stanza libraries.
“Technology gaps were also a problem, with some studies using suboptimal tools such as LIWC for POS tagging instead of more suitable alternatives like the spaCy 86 or Stanza 89 libraries.”
From Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review (2025) · cites this paper as [86]
The Discussion (page unavailable) proposes adding grammatical properties and parts of speech in a future study, rather than reporting POS tagging in this study. The Preprocessing section describes stop-word removal and stemming, not LIWC; the paper also does not compare LIWC with spaCy or Stanza. (LLM, conf=0.84)
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- Authors
- He, Q., Veldkamp, B. P., Glas, C. A. W., & de Vries, T.
- Affiliations
- University of Twente (NL)
- University of Twente, Enschede, Netherlands
- Corresponding author
- He, Qiwei
- Venue
- Assessment
- Publisher
- SAGE Publications
- Published
- 2016-07-28
- Volume
- 24
- Issue
- 2
- Pages
- 157-172
- ISSN
- 1073-1911 (ISSN-L)
- 1552-3489
- OpenAlex
- W2157394212
- Semantic Scholar
- ae482f77511e105bee20c88b275d047521a5d6c4
- Cited by
- 74
- References
- 65
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 11 · 2023: 12 · 2022: 15 · 2021: 7 · 2020: 6 · 2019: 6 · 2018: 2 · 2017: 1
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- green
- research.utwente.nl
- Locations
- Assessment (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Clinical Psychology › Posttraumatic Stress Disorder Research
- Keywords
- post-traumatic stress disorder, screening, automated diagnosis, text mining, natural language processing, n-gram features
- MeSH terms
- Adolescent, Adult, Algorithms, Decision Trees, Diagnosis, Computer-Assisted, Female, Humans, Mass Screening, Natural Language Processing, Personality Assessment, Stress Disorders, Post-Traumatic, Reproducibility of Results, Narration, Early Diagnosis, Data Mining, Self Report
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