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Practical AI for healthcare professionalsmachine learning with Numpy, Scikit-learn, and TensorFlow /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical AI for healthcare professionalsby Abhinav Suri.
其他題名:	machine learning with Numpy, Scikit-learn, and TensorFlow /
作者:	Suri, Abhinav.
出版者:	Berkeley, CA :Apress :2022.
面頁冊數:	xiv, 254 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligenceMedical applications.
電子資源:	https://doi.org/10.1007/978-1-4842-7780-5
ISBN:	9781484277805$q(electronic bk.)

Practical AI for healthcare professionalsmachine learning with Numpy, Scikit-learn, and TensorFlow /
Suri, Abhinav.

Practical AI for healthcare professionalsmachine learning with Numpy, Scikit-learn, and TensorFlow /[electronic resource] :by Abhinav Suri. - Berkeley, CA :Apress :2022. - xiv, 254 p. :ill., digital ;24 cm.

Chapter 1: Introduction to AI and its Use Cases- Chapter 2: Computational Thinking -- Chapter 3: Overview of Programming -- Chapter 4: A Brief Tour of Machine Learning Algorithms. -Chapter 5: Project #1 Neural Networks & Heart Disease -- Chapter 6: Project #2 CNNs & Brain Tumor Detection -- Chapter 7: The Future of Healthcare and AI.

Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, 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/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images. The topics covered in this book 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 those problems. 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.

ISBN: 9781484277805$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-7780-5doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / S87 2022

Dewey Class. No.: 610.28563

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520 $a Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, 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/Keras library along with Numpy and Scikit-Learn are covered as well. Once you've mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images. The topics covered in this book 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 those problems. 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.
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