Abstract
In this chapter, we introduce the topic of Natural Language Processing (NLP) in the clinical domain. NLP has shown increasing promise in tasks ranging from the assembly of patient cohorts to the identification of mental disorders. The chapter begins with a discussion of the necessity of NLP for analyzing EHRs. Subsequent sections then place clinical NLP research in a wider historical context by reviewing various approaches to NLP over time. The focus then turns to available NLP-related data resources and the methods of generating such resources. The actual development of NLP systems and their evaluation are then examined. The chapter concludes by describing current and future challenges in clinical NLP.
Keywords
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- 1.
Mathematically, the average inner-unit disagreement is given by
m u ,the number of annotators for u, d(x,y) is an arbitrarily defined function that quantifies dis-similarity between two values x and y, and a jk is the value assigned to the kth unit by the jth annotator. D o is then defined as
where there are n pairable values over m u annotators and N analysis units. Note each D u is weighted by the fraction of total annotations contained in analysis unit u.
D e is calculated by averaging over all annotated pairs:
where (i,u) ≠ (i’,u’) and m are the number of annotators. Random chance disagreement is thereby defined by the average disagreement over all pairs regardless of their analysis unit or annotator.
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Connolly, B. et al. (2016). Natural Language Processing – Overview and History. In: Hutton, J. (eds) Pediatric Biomedical Informatics. Translational Bioinformatics, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-10-1104-7_11
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