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Practical AI for Healthcare Professionals

Machine Learning with Numpy, Scikit-learn, and TensorFlow

Authors: Suri, Abhinav

  • Code and conceptualize practical AI projects for healthcare diagnosis of diabetes, heart disease, and brain cancer
  • Improve the lives of patients by developing new AI tooling even without a background in advanced software engineering
  • Push the boundaries of diagnosis with innovative AI-solutions
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Buy this book

eBook 29,99 €
price for Spain (gross)
  • Due: December 25, 2021
  • ISBN 978-1-4842-7780-5
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 39,51 €
price for Spain (gross)
  • Due: November 27, 2021
  • ISBN 978-1-4842-7779-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

Use Artificial Intelligence (AI) to analyze and diagnose what previously could only be handled by trained medical professionals. This book gives an introduction to practical AI, focusing on real-life medical problems, how to solve them with actual code, and how to evaluate the efficacy of these solutions. 

You’ll start by learning how to diagnose problems as ones that can and cannot be solved with AI or computer science algorithms. If you’re not familiar with those algorithms, that’s not a problem. You’ll learn the basics of algorithms and neural networks and when each should be applied. Then you’ll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The TensorFlow library alogn with Numpy and Scikit-Learn are covered, too. 

Once you’ve mastered those basic computer science concepts, you can dive into three projects with code, implementation details and explanation, and diagnostic utility analysis. These projects give you the change to explore using machine learning algorithms for diagnosing diabetes from patient data, using basic neural networks for heart disease prediction from cardiac data, and using convolutional networks for brain tumor segmentation from MRI scans

The topics and projects covered not only encompass areas of the medical field where AI is already playing a major role but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to problems using modern libraries, such as TensorFlow. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients.

What You'll Learn

  • Distinguish between problems that currently can and cannot be solved with AI
  • Master programming concepts not familiar to physicians, such as libraries, coding, and creating and training ML models
  • Perform dataset analysis with decision trees, SVMs, and neural networks.

Who This Book Is For

Physicians and other healthcare professionals  curious about AI and interested in leading medical innovation initiatives. Additionally, software engineers working on healthcare related projects involving AI.

About the authors

Abhinav “Abhi” Suri is a current medical student at the UCLA David Geffen School of Medicine. He completed his undergraduate degree at the University of Pennsylvania with majors in Computer Science and Biology. He also completed a Masters in Public Health (in Epidemiology) at Columbia University Mailman School of Public Health. Abhihas been dedicated to exploring the intersection between computer science and medicine. As an undergraduate, he carried out and directed research on deep learning algorithms for the detection of vertebral deformities and the detection of genetic factors that increase risk of COPD. His public health research focused on opioid usage trends in NY State and the development/utilization of geospatial dashboards for monitoring demographic disease trends in the COVID-19 pandemic.

 

Outside of classes and research, Abhi is an avid programmer and has made applications that address healthcare worker access in Tanzania, aid the discovery process for anti-wage theft cases, and facilitate access to arts classes in underfunded school districts. He also developed (and currently maintains) a popular open-source repository, Flask-Base, which has over 2,000 stars on Github. He also enjoys teaching (lectured a course on JavaScript) and writing. So far, his authored articles and videos have reached over 200,000 people across a variety of platforms.

Buy this book

eBook 29,99 €
price for Spain (gross)
  • Due: December 25, 2021
  • ISBN 978-1-4842-7780-5
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 39,51 €
price for Spain (gross)
  • Due: November 27, 2021
  • ISBN 978-1-4842-7779-9
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Practical AI for Healthcare Professionals
Book Subtitle
Machine Learning with Numpy, Scikit-learn, and TensorFlow
Authors
Copyright
2022
Publisher
Apress
Copyright Holder
Abhinav Suri
eBook ISBN
978-1-4842-7780-5
DOI
10.1007/978-1-4842-7780-5
Softcover ISBN
978-1-4842-7779-9
Edition Number
1
Number of Pages
XIV, 254
Number of Illustrations
45 b/w illustrations
Topics