Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector

Abstract

Due to the exponential increase in population, the healthcare sector is also booming. The data generated from the healthcare sector is too huge nowadays, and processing all of it manually is a tedious task. The major types of data, which we want to process in the healthcare sector include discharge summaries, reports from radiologists or pathologists, case notes of the physicians, etc. Nowadays, mostly, all these details are available in digital form, stored in some of the healthcare systems. In most cases, all these reports will be in an unstructured form and processing it using conventional computer algorithms is not efficient. Linguistics and computer science knowledge is required for natural language processing (NLP). The usage of NLP in the healthcare sector is widespread, allowing us to infer the meaning of unstructured healthcare information and translate it into a form that the electronic healthcare system can understand. This will help to reduce the manpower requirement in the healthcare industry, enable more structured details to be stored and reused in future for reference, and reduce the chances of human errors while processing. This manuscript gives an overview of NLP and its various phases, the various applications of NLP, the key areas where NLP can use in the healthcare sector, various well-known NLP-based approaches available for processing healthcare information, and the various datasets available for performing research in the domain of NLP on healthcare information. This manuscript also gives a brief idea about the research challenges in this domain, which need future attention.

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