Title:A Generic Integrated Framework of Unsupervised Learning and Natural Language Processing Techniques for Digital Healthcare: A Comprehensive Review and Future Research Directions
Volume: 18
Issue: 2
Author(s): Kibballi Aditya Shastry*
Affiliation:
- Department of Information Science and Technology, Nitte Meenakshi Institute of Technology, Bengaluru, 560064, India
Keywords:
Digital healthcare, machine learning, unsupervised learning, natural language processing, wearable sensors, data analysis.
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.
