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The disability determination process of the Social Security Administration's (SSA's) disability program requires assessing work-related functioning for individual claimants alleging disability due to mental impairment. This task is particularly challenging because the determination process involves the review of a large file of information, including objective medical evidence and self-reports from claimants, families, and former employers. To improve this decision-making process, SSA entered an interagency agreement with the Rehabilitation Medicine Department, Epidemiology and Biostatistics Section, in the Clinical Center of the National Institutes of Health, intending to use data science and informatics to develop decision support tools. This collaborative effort over the past decade has led to the development of the Work Disability-Functional Assessment Battery and has initiated an approach to applying natural language processing to the review of claimants' files for information on mental health functioning. This informatics research collaboration holds promise for improving the process of disability determination for individuals with mental impairments who make claims at the SSA.

The field of clinical natural language processing (NLP) has been built on the analysis of clinical sublanguage characteristics. It is well recognized that not only does clinical sublanguage differ from general English (or other languages) but also clinical sublanguage differs among clinical subspecialties and among corpora originating from different healthcare systems. A less recognized aspect is that clinical sublanguage, like all languages, evolves over time. This paper analyses the evolution of clinical sublanguage using a large, national clinical text corpus spanning 15 years. Through the analyses of document types, length, ngrams, and concepts, we found strong evidence that clinical sublanguage does evolve and such changes have implications for NLP development and maintenance. Although the analysis is performed on one corpus, our observations of sublanguage changes are generalizable.

Irregular spellings in clinical free text present challenges to natural language processing. A number of spelling correction tools exist, but automated spelling correction of clinical text is not a routine practice due to the risk of introducing new errors. We developed a novel spelling analysis application that combines Word2Vec and Levenshtein Edit Distance Constraints to identify variant forms of words. The use case applied to this study was that of discovering herbal and dietary supplements that interact with prescription medications in clinical text. The prototype application processed a large corpus (approximately 1.6 million records), achieving a positive predictive value of 0.9322, in identifying spelling variants, outperforming two baseline methods that achieved positive predictive values of 0.0348 and 0.0067. Our findings suggest that this prototype application provides a more efficient method for researchers and clinicians to find valid misspellings of terms in clinical text.

Objectives Normalizing mentions of medical concepts to standardized vocabularies is a fundamental component of clinical text analysis. Ambiguity—words or phrases that may refer to different concepts—has been extensively researched as part of information extraction from biomedical literature, but less is known about the types and frequency of ambiguity in clinical text. This study characterizes the distribution and distinct types of ambiguity exhibited by benchmark clinical concept normalization datasets, in order to identify directions for advancing medical concept normalization research. Materials and Methods We identified ambiguous strings in datasets derived from the 2 available clinical corpora for concept normalization and categorized the distinct types of ambiguity they exhibited. We then compared observed string ambiguity in the datasets with potential ambiguity in the Unified Medical Language System (UMLS) to assess how representative available datasets are of ambiguity in clinical language. Results We found that Discussion Existing datasets are not sufficient to cover the diversity of clinical concept ambiguity, limiting both training and evaluation of normalization methods for clinical text. Additionally, the UMLS offers important semantic information for building and evaluating normalization methods. Conclusions Our findings identify 3 opportunities for concept normalization research, including a need for ambiguity-specific clinical datasets and leveraging the rich semantics of the UMLS in new methods and evaluation measures for normalization.