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Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions(Healthcare Technologies)

Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions(Healthcare Technologies)

          
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About the Book

A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs. Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed. This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.

Table of Contents:
Chapter 1: COVID-19 detection in X-ray images using customized CNN model Chapter 2: Introducing deep learning in medical diagnosis Chapter 3: Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML) Chapter 4: Classification methodologies in healthcare Chapter 5: Introducing deep learning in medical domain Chapter 6: Deep-stacked autoencoder for medical image classification Chapter 7: Comparison of machine learning and deep learning algorithms for prediction of coronary heart disease Chapter 8: Revolution in technology-enabled healthcare: Internet of Things Chapter 9: Smart healthcare monitoring framework using IoT with big data analytics Chapter 10: Experimental analysis and investigation of dementia detection framework using EHR-based variant LSTM model Chapter 11: An intelligent agent-based distributed patient scheduling using token-based coordination approach: a case study Chapter 12: Internet of Things (IoT) for the efficient healthcare system Chapter 13: Comprehension of melody representation and speed-up approaches for query by humming system Chapter 14: Python for digital health solutions: elevated outcomes Chapter 15: IoT-enabled healthcare - a paradigm shift Chapter 16: IoT-based cardiovascular prediction framework using deep learning algorithms Chapter 17: An intelligent approach using convolutional neural network (CNN) for early detection of melanoma and other skin diseases Chapter 18: Self-organizing deep learning approach for controlling movements of wheeled apparatus through corneal connotation Chapter 19: Prediction of breast tumour outcome to chemotherapy using statistical MR images through deep learning approaches Chapter 20: Risk analysis and prediction of cancer associated with Type II diabetes: a review


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Product Details
  • ISBN-13: 9781839535116
  • Publisher: Institution of Engineering and Technology
  • Publisher Imprint: Institution of Engineering and Technology
  • Height: 234 mm
  • No of Pages: 420
  • Series Title: Healthcare Technologies
  • Sub Title: New AI techniques for real-time interventions
  • Width: 156 mm
  • ISBN-10: 1839535113
  • Publisher Date: 25 Oct 2022
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Spine Width: 25 mm
  • Weight: 815 gr


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