Explainable Machine Learning in Medicine (Synthesis Lectures on Engineering, Science, and Technology)

Price: $89.99 – $85.49
(as of Dec 24,2024 20:58:27 UTC – Details)

Publisher ‏ : ‎ Springer (December 15, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 100 pages
ISBN-10 ‏ : ‎ 3031448790
ISBN-13 ‏ : ‎ 978-3031448799
Item Weight ‏ : ‎ 8.2 ounces
Dimensions ‏ : ‎ 6.61 x 0.23 x 9.45 inches

Explainable Machine Learning in Medicine: A Comprehensive Guide

Machine learning algorithms have shown great promise in revolutionizing the field of medicine, from diagnosing diseases to predicting patient outcomes. However, one major challenge in adopting these algorithms in clinical practice is their lack of transparency and interpretability. This is where explainable machine learning comes in.

In the book “Explainable Machine Learning in Medicine” from the Synthesis Lectures on Engineering, Science, and Technology series, authors delve into the importance of explainability in healthcare and provide insights into how machine learning models can be made more interpretable for physicians and healthcare professionals.

The book covers various topics, including:

– The importance of explainability in healthcare: The authors explain why it is crucial for machine learning models to be transparent and interpretable in medical settings, where decisions can have life-changing consequences.

– Techniques for making machine learning models explainable: The book explores different methods and approaches for improving the interpretability of machine learning models, such as feature importance analysis, model-agnostic explanations, and rule-based models.

– Case studies and real-world applications: The authors showcase examples of how explainable machine learning has been successfully applied in healthcare, from predicting patient readmissions to identifying high-risk individuals for preventive interventions.

Overall, “Explainable Machine Learning in Medicine” serves as a comprehensive guide for healthcare professionals, data scientists, and researchers interested in harnessing the power of machine learning in medicine while ensuring accountability and transparency. By making machine learning models explainable, we can build trust in these technologies and ultimately improve patient care and outcomes.
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