A Generic Integrated Framework of Unsupervised Learning and Natural Language Processing Techniques for Digital Healthcare: A Comprehensive Review and Future Research Directions

Abstract

The increasing availability of digital healthcare data has opened up fresh prospects for improving healthcare through data analysis. Machine learning (ML) procedures exhibit great promise in analyzing large volumes of healthcare data to extract insights that could be utilized to improve patient outcomes and healthcare delivery. In this work, we suggest an integrated framework for digital healthcare data analysis by integrating unsupervised learning techniques and natural language processing (NLP) techniques into the analysis pipeline. The module on unsupervised learning will involve techniques, such as clustering and anomaly detection. By clustering similar patients together based on their medical history and other relevant factors, healthcare providers can identify subgroups of patients who may require different treatment approaches. Anomaly detection can also help to detect patients who stray from the norm, which could be indicative of underlying health issues or other issues that need additional investigation. The second module on NLP will enable healthcare providers to analyze unstructured text data such as clinical notes, patient surveys, and social media posts. NLP techniques can help to identify key themes and patterns in these datasets, requiring awareness that could not be readily apparent through other means. Overall, incorporating unsupervised learning techniques and NLP into the analysis pipeline for digital healthcare data possesses the promise to enhance patient results and lead to more personalized treatments, and represents a potential domain for upcoming research in this field. In this research, we also review the current state of research in digital healthcare information examination with ML, including applications like forecasting clinic readmissions, finding cancerous tumors, and developing personalized drug dosing recommendations. We also examine the potential benefits and challenges of utilizing ML in healthcare data analysis, including issues related to data quality, privacy, and interpretability. Lastly, we discuss the forthcoming research paths, involving the necessity for enhanced methods for incorporating information from several resources, developing more interpretable ML patterns, and addressing ethical and regulatory challenges. The usage of ML in digital healthcare data analysis promises to transform healthcare by empowering more precise diagnoses, personalized treatments, and improved health outcomes, and this work offers a complete overview of the current trends.