Natural Language Processing in Medical Science and Healthcare
Indu Bala*
Published: December 04, 2022
DOI: 10.55162/MCMS.04.088
Abstract
Natural Language Processing (NLP) is a branch of Machine Learning (ML) that primarily aims to reduce the distance between human and machine capabilities. We focus on designing and building applications and systems in a way that allows an easy intersection between computers and natural languages developed for human use. NLP has been used in several areas within artificial intelligence (AI) and data processing application such as social media, translation, summarization, and medical applications. In fact, in the last decades, it has been exhaustively used in current technology to support spam email privacy, personal voice assistants language, translation application, and big data management. Although the medical field is one of the richest in terms of big unstructured data it has not been well invested so far. The analysis of medical data such as patient reports, doctor notes, patient demography details, lab test results, and previous medical history can be effectively examined through NLP. It can improve the treatment and patient service within a reasonable cost and time. NLP helps to train a machine to convert unstructured data into structured and can suggest a decision in the diagnosis process [1].
Doctors spend a lot of time inputting how and what happens to their patients into notes. These case notes are stored as free text in the Electronic Health Record Systems (EHRs). A considerable volume of patient data is inputted into EHRs on daily basis. Out of that, only 20% of data are structured and the rests are unstructured and therefore go largely unutilized. Since the extraction and mining of large unstructured data are challenging and resource-intensive. Without NLP technology the data is not in a usable format for modern computer-based algorithms to extract [2].