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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Using statistical text mining to supplement the development of an ontology
Stephen Luther1, Donald Berndt2, Dezon Finch1
1Consortium for Healthcare Informatics Research (CHIR); VA HSR&D/RR&D Center of Excellence: Maximizing Rehabilitation Outcomes, Tampa, FL, United States.
Journal of Biomedical Informatics
|November 22, 2011
Summary
Statistical text mining enhanced the development of a clinical vocabulary for post-traumatic stress disorder (PTSD). This approach identified 226 unique PTSD-related terms from veteran clinical notes, supporting existing methods.
Area of Science:
- Clinical Informatics
- Natural Language Processing
- Psychiatry
Background:
- Developing a comprehensive clinical vocabulary for post-traumatic stress disorder (PTSD) is crucial for accurate diagnosis and treatment within the Veterans Affairs (VA) system.
- Existing methods for vocabulary development, such as literature reviews and clinician focus groups, can be supplemented by advanced analytical techniques.
- The need for efficient and precise methods to extract relevant clinical terms from large datasets of patient notes is recognized.
Purpose of the Study:
- To utilize statistical text mining (STM) to augment the creation of a clinical vocabulary specifically for post-traumatic stress disorder (PTSD).
- To identify and validate unique PTSD-related terms from electronic health records within the VA.
- To compare the efficacy of STM methods against traditional approaches in clinical vocabulary development.
Main Methods:
- Collected outpatient progress notes from 405 veterans with PTSD and 392 with other psychological conditions.
- Employed 'multi-model term scoring' using stepwise logistic regression with varied weighting options.
- Utilized 'iterative term refinement' involving a stop list and clinical review to filter terms.
Main Results:
- A combined approach of multi-model term scoring and iterative term refinement yielded 226 unique PTSD-related terms.
- The identified terms were reviewed and validated by two clinical experts.
- Results demonstrated that STM analyses significantly contributed to and supported ongoing vocabulary development efforts.
Conclusions:
- Statistical text mining is a valuable tool for supplementing traditional methods in developing specialized clinical vocabularies, such as for PTSD.
- The identified 226 terms provide a robust foundation for enhancing clinical documentation and research related to PTSD.
- STM methods offer a data-driven approach that complements expert clinical review and literature-based findings.
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