Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

By J. Strauss, C. Chao, M. Kwan, Syed A. Ahmed, J. Schottinger, V. Quinn. 2012

68 citations recorded by the source corpus.

Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem. Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report. Results Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups. Discussion Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability. Conclusion SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.

Subfields: Artificial Intelligence, Biomedical Engineering, Molecular Biology

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Citations in the corpus

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Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, background): “Therefore, NLP is necessary to identify information that is contained in text but may be inconsistently captured in the structured data, such as recurrence in cancer [20, 38], whether a patient smokes [5], classification within the autism spectrum [39], or drug treatment patterns [40].”