Название: Healthcare Recommender Systems: Techniques and Recent Developments
Автор: Simar Preet Singh, Deepak Kumar Jain, Johan Debayle
Издательство: Springer
Год: 2025
Страниц: 379
Язык: английский
Формат: pdf (true)
Размер: 20.5 MB

The book explores the complete system perspective, underlying theories, modelling, and the applications of pattern recognition in Healthcare Recommender System. Considering the interest of researchers and academicians, editors here aim to present this book in a multidimensional perspective that will be covering Healthcare Recommender Systems in depth, considering pattern recognition techniques using amalgamation of emerging technologies. It aims to cover all topics ranging from discussion of recommender system to efficient management to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and in order to achieve this, the book has been organized into various chapters.

The prime focus of the book is to explore the various issues, challenges, and research directions of pattern recognition in Healthcare Recommender Systems. The table of contents is designed in a manner so as to provide the reader with a broad list of its applications. Additionally, the book addresses the transformations in the area of Healthcare Recommender Systems. Thus, the book plans to discuss the recent research trends and advanced topics in the field of healthcare automation system which will be of interest to industry experts, academicians and researchers working in this area. Hence, the editors aim is to cover diversity in the domain while achieving completeness.

The healthcare data management landscape is currently seeing a significant transformation due to the combination of Blockchain and AI technologies, which is driving a revolutionary shift. The Recommender Diagnostic Computer System is a cutting-edge computer system that utilizes sophisticated algorithms to provide recommendations or suggestions for diagnoses. It takes into account input data, including medical records and diagnostic images. Most likely, this system smoothly integrates Artificial Intelligence (AI) algorithms, acting as a guiding influence for healthcare practitioners to make precise and effective diagnostic conclusions.

In recent years, the intersection of healthcare and technology has given rise to innovative solutions that are revolutionizing the way healthcare services are delivered. Among these cutting-edge advancements, Healthcare Recommender Systems have emerged as powerful tools, poised to transform the healthcare landscape by providing personalized and evidence-based recommendations to patients, clinicians, and healthcare providers. Leveraging the competences of Machine Learning (ML) procedures, Big Data analytics, and patient-specific information, these systems hold the promise of improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare resource allocation. At the heart of every HRS lies the procedure of information gathering and preprocessing. However, the healthcare domain presents unique challenges and considerations, such as privacy and security concerns, that necessitate careful handling of sensitive patient information. Understanding and addressing these issues is fundamental to developing trustworthy and ethically sound recommender systems that can foster a patient-centric approach to care.

The suggested system is constructed using IPFS for off-chain storage, permissioned Hyperledger Fabric, Blockchain, and Hyperledger Composer. Dapp through Hyperledger composer is used for front-end communication. A web user interface built using Python, HTML, CSS, Node JS, Kafka Ordering Service, Keras, and TensorFlow Backend has been used for the entire implementation process.