Advances of Machine Learning for Knowledge Mining in Electronic Health Records
eBook - ePub

Advances of Machine Learning for Knowledge Mining in Electronic Health Records

  1. 304 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Advances of Machine Learning for Knowledge Mining in Electronic Health Records

About this book

The book explores the application of cutting-edge machine learning and deep learning algorithms in mining Electronic Health Records (EHR). With the aim of improving patient health management, this book explains the structure of EHR consisting of demographics, medical history, and diagnosis, with a focus on the design and representation of structured, semi-structured, and unstructured data.

  • Explains the design of organized, semi-structured, unstructured, and irregular time series data of electronic health records
  • Covers information extraction, standards for meta-data, reuse of metadata for clinical research, and organized and unstructured data
  • Discusses supervised and unsupervised learning in electronic health records
  • Describes clustering and classification techniques for organized, semi- structured, and unstructured data from electronic health records

This book is an essential resource for researchers and professionals in fields like computer science, biomedical engineering, and information technology, seeking to enhance healthcare efficiency, security, and privacy through advanced data analytics and machine learning.

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Yes, you can access Advances of Machine Learning for Knowledge Mining in Electronic Health Records by P. Mohamed Fathimal,T. Ganesh Kumar,J. B. Shajilin Loret,Venkataraman Lakshmi,Manish T. I. in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Cloud Computing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Table of Contents
  6. Preface
  7. Editor Biography
  8. List of Contributors
  9. 1 An Introduction to Electronic Health Records
  10. 2 Challenges and Strategies for Extracting Secure Patterns by Using EHR
  11. 3 The Art of Organizing EHR Data: A Classification Journey Through Structured, Unstructured, and Semi-Structured Records
  12. 4 A Blockchain Enabled Framework for Electronic Health Records
  13. 5 Cardio Vascular Disease Diagnosis using Deep Learning Models
  14. 6 A Computational Analysis for the Diagnosis of Schizophrenia Disease using Machine Learning Methods
  15. 7 Predicting Lung Cancer using Supervised Algorithms: A Machine Learning Approach
  16. 8 Article Summarising the Application of Artificial Intelligence and Machine Learning Techniques to Several Forms of Electronic Health Records
  17. 9 Machine Learning Techniques to Predict the Risk of Chronic Obstructive Pulmonary Disease
  18. 10 Dynamic Learning Scheduling Algorithm and Multilayer Perceptron Model for Heart Disease Prediction System
  19. 11 Efficient Heart Disease Prediction using IBM Cloud Storage with Auto AI Service
  20. 12 Electronic Health Records: A Survey
  21. Index