Advances in natural language processing for healthcare: A comprehensive review of techniques, applications, and future directions

Abstract

Natural Language Processing (NLP) techniques have gained significant traction within the healthcare domain for analyzing textual healthcare-related datasets, sourced primarily from Electronic Health Records (EHR) and increasingly from social networks. This study delves into applying NLP technologies within the healthcare sector, drawing insights from textual datasets from various sources. It reviews the relevant articles from 2019 to 2023 and compares the pertinent solutions included therein. In addition, it explores the various NLP technologies used for processing healthcare datasets in multiple languages. The review focuses on existing studies related to various medical conditions, including cancer and chronic and infectious diseases. It categorizes these cutting-edge studies into four different NLP task categories: prediction and detection, text analysis and modeling, information processing, and other healthcare applications. Notably, the findings reveal that the most prevalent NLP tasks employed in healthcare revolve around risk prediction and text classification. Moreover, the study identifies a pressing need for more extensive research that encompasses the utilization of non-textual medical datasets from EHR, such as X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) scans. A key observation is that much of the current research studies about NLP related to the healthcare field were primarily using conventional data processing methods, such as ML and DL techniques. Despite their success, these methods frequently have several distinct limitations as they are not able to handle large-scale, complex datasets. In contrast, there is less focus on sophisticated technologies such as big data analytics and transformer-based modeling. Big data analytics can manage massive amounts of unstructured data from sources such as EHRs and social media, providing a more comprehensive insight into healthcare patterns. Transformer models, like BERT and GPT, are designed to detect complex patterns and contextual relationships in text, making them particularly useful for medical text classification, sentiment analysis, and disease prediction. Current research studies have not fully explored the potential of these advanced technologies, which could significantly increase the efficiency and scalability of natural language processing applications in healthcare. This highlights opportunities for further exploration and innovation within the domain of NLP in healthcare.