Data Science in the Medical Field

Seifedine Kadry, Shubham Mahajan

• Publisher: Academic Press
• Year: 2024
• Topic: Economics
• eBook ISBN:
  9780443240294
• Subtopic: Econometrics
• Index: Economics

Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. - Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications - Combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologies - Provides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book

Table of contents

• Cover
• Title page
• Table of Contents
• Copyright
• Contents
• List of contributors
• About the authors
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata
• Chapter 2 : An automatic detection and severity levels of COVID-19 using convolutional neural network models
• Chapter 3 : Biosensors and disease diagnostics in medical field
• Chapter 4 : Brain tumor recognition and classification techniques
• Chapter 5 : Identifying the features and attributes of various artificial intelligence-based healthcare models
• Chapter 6 : Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
• Chapter 7 : A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
• Chapter 8 : Predictive analysis in healthcare using data science: leveraging big data for improved patient care
• Chapter 9 : Data science in medical field: advantages, challenges, and opportunities
• Chapter 10 : Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
• Chapter 11 : Machine learning in heart disease prediction
• Chapter 12 : U-Net-based approaches for brain tumor segmentation
• Chapter 13 : Explainable image recognition models for aiding radiologists in clinical decision making
• Chapter 14 : Prediction of heart failure disease using classification algorithms along with performance parameters
• Chapter 15 : Cancer survival prediction using artificial intelligence: current status and future prospects
• Chapter 16 : Heart disease prediction in pregnant women with diabetes using machine learning
• Chapter 17 : Healthcare using image recognition technology
• Chapter 18 : Integration of deep learning and blockchain technology for a smart healthcare record management system
• Chapter 19 : Internet of things based smart health and attendance monitoring system in an institution for COVID-19
• Chapter 20 : Medical diagnosis using image processing techniques
• Chapter 21 : Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
• Chapter 22 : Predictive analysis in healthcare using data science
• Chapter 23 : Recommender systems in healthcare—an emerging technology
• Chapter 24 : Robotics: challenges and opportunities in healthcare
• Chapter 25 : A new era of the healthcare industry using Internet of Medical Things
• Chapter 26 : Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
• Chapter 27 : Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review
• Author Index
• Subject Index