Advances of Machine Learning for Knowledge Mining in Electronic Health Records

By: P. Mohamed Fathimal

Write A Review

eText | 7 March 2025 | Edition Number 1

At a Glance

Format
ePUB

eText

$71.49

or 4 interest-free payments of $17.87 with

Instant online reading in your Booktopia eTextbook Library *

Read online on

Desktop

Tablet

Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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.