SAS-Based NLP for Identifying Incident and Recurrent Malignancies

1. May 1, 2012 •2012 HMORN Conference • Seattle, Washington Use of SAS-Based Natural Language Processing to Identify Incident and Recurrent Malignancies Justin A. Strauss, MA Research Associate III Kaiser Permanente Southern California

2. Co-Authors • Chun R. Chao, PhD • Marilyn L. Kwan, PhD • Syed A. Ahmed, MD • Joanne E. Schottinger, MD • Virginia P. Quinn, PhD

3. Acknowledgements & Funding • Mayra Martinez, Michelle McGuire, Melissa Preciado, Nirupa Ghai, and Jeff Slezak (KPSC); Lawrence Kushi (KPNC); Debra Ritzwoller (KPCO); Joan Warren (NCI); Jianyu Rao and Jiaoti Huang (UCLA) • Funding was provided by KPSC Community Benefit and the Cancer Research Network

4. Malignancy Identification • Malignancy identification is important for clinical and epidemiologic cancer research. • Limited quality and availability of incident and recurrent malignancy data within health plans. • Delayed availability of incident malignancy data from cancer registries. • Few registries track cancer recurrences. • Manual chart abstraction slow and expensive. • Previous research has shown electronic diagnosis codes (e.g., ICD-9) to be unreliable.

5. Natural Language Processing • Natural language processing (NLP) can be used to identify and extract information from electronic clinical text, including incident and recurrent malignancy data. • Increasing opportunity for NLP with adoption of electronic clinical systems in patient care delivery. • Despite its potential value in clinical and research settings, NLP usage has been relatively sparse. Contributing factors may include: • Technical complexity • Systems integration requirements • Habitual use of existing methods

6. SCENT Overview • A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to identify incident and recurrent malignancies using text from pathology reports. • SCENT is currently being implemented in two research studies at Kaiser Permanente Southern California (KPSC): • Intervention to improve medication adherence among breast cancer patients. • Differences in the prognosis of prostate cancer patients according to their genetic factors • Use of SAS programming minimizes implementation barriers and increases availability for multisite research.

7. Description of Methods • SCENT identifies non-negated clinical concepts within pathology report text. • Built using SAS Base (does not require Text Miner add-on). • Makes extensive use of SAS hash objects and regular expressions. • Includes components for preprocessing, matching, negation and uncertainty detection, extracting diagnostic information (e.g., staging and Gleason score), and classifying report malignancy status. • Flexibility to assign codes using variety of coding systems. • Validation used subset of SNOMED 3.x (~1000 concepts).

8. SCENT Process Diagram

9. Sample Report Coding Preprocessed Text Coded Text

10. Validation Study • To validate SCENT, trained chart abstractors reviewed electronic pathology reports. • Random samples of breast (n=400) and prostate (n=400) cancer patients. • Patients diagnosed at KPSC between 2000-2007. • Reports included from six months post-diagnosis through end of 2008. • In total, 206 breast and 186 prostate cancer patients contributed 490 and 425 eligible reports, respectively. • SCENT classifications were compared with those of abstractors.

11. Classification Concordance Note: incident contralateral breast malignancies were considered to be recurrences.

12. SCENT Performance Metrics * Shown with Wilson's 95% confidence interval.

13. Conclusions • Favorable results suggest SCENT can identify and extract information about primary and recurrent malignancies from pathology reports. • Rapid cancer case identification. • Improved measurement accuracy of common study endpoint. • SCENT has the potential to expedite chart reviews by narrowing the search and highlighting relevant concepts. • Generalized utility for extracting standardized disease scores and other clinical information. • SCENT is proof of concept for SAS-based NLP that can be easily shared between institutions to support research.

14. Limitations & Next Steps • SCENT has a number of limitations, including: • Unable to disambiguate and contextualize identified clinical concepts without part-of-speech (POS) tagging. • More susceptible to changes in text structure and increased linguistic variability than statistical NLP approaches. • General purpose NLP (e.g., cTAKES) likely to perform better outside of pathology. • Next steps include: • Release SCENT source code and requisite support files. • Optimize current functionality and assess feasibility of adding methods (e.g., POS tagging, n-grams, statistical classifiers). • Attempt to identify non-pathologically diagnosed malignancies using radiology reports and clinical progress notes. • Quantify cost savings associated with SCENT-assisted chart reviews.

15. Questions?