An integrated deep learning and natural language processing approach for continuous remote monitoring in digital health

Abstract

The rapid adoption of digital health technologies generates a vast amount of health data, presenting an opportunity to leverage advanced data analytics and cognitive computing to extract insights and knowledge from this data and improve healthcare outcomes. This research presents an integrated deep learning (DL) and natural language processing (NLP) approach for continuous remote monitoring in digital health. The approach involves using DL algorithms to analyze data collected from wearable devices and remote monitoring tools, while NLP is used to analyze patient feedback and electronic medical records. Various DL algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are suggested to analyze medical images and time-series data, and unsupervised learning techniques, such as autoencoders, are recommended for feature extraction. We provide a comparison between our proposed approach and previous related works. We also identify and discuss the limitations of existing works and explain how our approach overcomes these limitations. The study also delivers a review of the prevailing work of data analytics and cognitive computing in digital health, examining the applications of these technologies in various areas of healthcare, identifying the challenges and opportunities of implementing them in healthcare, and proposing research directions that can help accelerate their adoption. The proposed approach can enable health professionals to remotely monitor patients’ health conditions in real-time and predict the likelihood of adverse health events, leading to improved patient outcomes, reduced healthcare costs, and enhanced quality of care. This paper provides insights into current trends, challenges, and research directions in digital health, informing future research and development.