NATURAL LANGUAGE PROCESSING (NLP) APPLICATIONS IN HEALTHCARE: EXTRACTING VALUABLE INSIGHTS FROM UNSTRUCTURED MEDICAL DATA

Abstract

This paper delves into the exploration of utilizing the knowledge derived from NLP for text-based EHRs. It encompasses an extensive list of the advantages, obstacles, and optimal approaches in the most recent NLP language models. Additionally, it presents various healthcare NLP use cases and the innate vocabulary found in clinical text, along with real-world instances of NLP applications. Furthermore, this paper delves into the empirical discoveries and the prospective outlook, alongside strategies for maximizing the advantages derived from NLP for unstructured EHR text. In the concluding remarks, it is envisioned that NLP will play a pivotal role in contributing to future healthcare breakthroughs and innovations that can genuinely have a positive impact on the lives of countless individuals.The healthcare industry is now inundated with vast amounts of structured as well as unstructured healthcare data. Electronic Health Record (EHR) provides a valuable source to access these large volumes of patient information. Natural Language Processing (NLP) is a set of techniques and algorithms designed specifically to retrieve and analyze the information stored in EHR, as information stored here is unstructured. The interpretation of Natural Language Processing also enables the extraction of information such as the identification of ailments, manifestation of symptoms, diagnostics, and current medical conditions by removing the constraints of predefined and fixed elements. NLP can be utilized to execute various operations on EHR [1]. These operations include identifying the conditions from existing data, deriving results from EHR, standardization of terms, and handling spelling errors. Healthcare research is fundamental so as to help in improving the quality of healthcare through the availability of information to augment patient’s net well-being and comply with the necessary changes in health systems. EHRs are useful in this regard as they perform a very central role in supporting information about patients in hospitals. This, in turn, assists data scientists in deriving crucial insights to make necessary decisions in enhancing the patients’ treatments. However, its unstructured character of medical data is a major issue in analytics of large datasets. Fortunately, constant progress has been made in the NLP models, and that enhanced effective algorithms for translation of the words to meaningful data that are valuable to patient’s care has been developed to bring a drastic change in the way the health care data is managed for the patients and other health care givers [1]. NLP has been applied in the healthcare field and the results have been impressive in terms of mining useful information from structural medical records. Subsequently, thanks to the analysis of the peculiarities of written and spoken language with the help of NLP technology, patient needs, the effectiveness of treatments, and general tendencies in the sphere of healthcare can be comprehensively assessed. This has changed decision-making activities, patient-centered care, and advancement in healthcare research and innovation. Since the area of NLP is progressing more and more, it is crucial to emphasize that the alternatives for its utilization in the sphere of healthcare are becoming more apparent [2].