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Chapman and Hall/CRC Applied Machine Learning in Healthcare: Case-Based Approach

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Description

This comprehensive guide from Chapman and Hall/CRC explores practical applications of machine learning within healthcare settings through real-world case studies. The case-based approach enables readers to understand how machine learning algorithms solve actual clinical and operational challenges, from diagnostic imaging to patient outcome prediction and treatment optimisation. Ideal for healthcare professionals, data scientists, and computer science students, this book bridges the gap between theoretical machine learning concepts and their implementation in medical contexts. By examining concrete examples and practical scenarios, readers gain insights into deploying machine learning solutions that improve patient care, operational efficiency, and clinical decision-making. The structured case-by-case methodology provides both the technical foundation and practical wisdom needed to navigate the complexities of machine learning in healthcare environments.

Key Features

Applied Machine Learning in Healthcare delivers case-based learning that demonstrates real-world machine learning implementations across diverse medical and clinical domains.

Chapman and Hall/CRC presents content structured around concrete healthcare scenarios, enabling readers to understand algorithm applications and outcomes in practical settings.

The case-based approach covers machine learning techniques for diagnostics, patient risk assessment, treatment planning, and healthcare system optimisation.

This book serves healthcare professionals, data scientists, and students seeking to understand how machine learning solves genuine challenges in medical practice and research.

Readers gain practical knowledge transferable to healthcare AI projects, including implementation considerations and lessons learned from actual case deployments.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
29 December 2025
Listed Since
12 July 2025

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