Advances of Machine Learning for Knowledge Mining in Electronic Health Records

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Advances of Machine Learning for Knowledge Mining in Electronic Health Records

Fathimal, P. Mohamed (EDT)/ Kumar, T. Ganesh (EDT)/ Loret, J. B. Shajilin (EDT)

ウェブストア価格 ¥40,832（本体¥37,120）
Chapman & Hall/CRC（2025/03発売）
外貨定価 US$ 200.00
ポイント 371pt

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製本 Hardcover:ハードカバー版／ページ数 270 p.
言語 ENG
商品コード 9781032526102
DDC分類 006.31

Full Description

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.

1. An Introduction to Electronic Health Records 2. Challenges and Strategies for Extracting Secure Patterns by Using EHR 3. The Art of Organizing EHR Data: A Classification Journey Through Structured, Unstructured, and Semi-Structured Records 4. A blockchain Enabled Framework for Electronic Health Records 5. Cardio Vascular Disease Diagnosis using Deep Learning models 6. A Computational Analysis for the Diagnosis of Schizophrenia Disease Using Machine Learning Methods 7. Predicting Lung Cancer Using Supervised Algorithms:A Machine Learning Approach 8. Article summarising the application of Artificial Intelligence and Machine Learning Techniques to several forms of Electronic Health Records 9. Machine Learning Techniques to Predict the Risk of Chronic Obstructive Pulmonary Disease 10. Dynamic Learning Scheduling Algorithm and Multilayer Perceptron Model for Heart Disease Prediction System 11. Efficient Heart Disease Prediction using IBM Cloud Storage with Auto AI Service 12. Electronic Health Records-A survey