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AI for healthcare with Keras and Ten...

Anshik.

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AI for healthcare with Keras and Tensorflow 2.0 = design, develop, and deploy machine learning models using healthcare data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI for healthcare with Keras and Tensorflow 2.0/ by Anshik.
Reminder of title:	design, develop, and deploy machine learning models using healthcare data /
Author:	Anshik.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xvi, 381 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1: Healthcare Market: A Primer -- Chapter 2: Introduction and Setup -- Chapter 3: Predicting Hospital Readmission by Analyzing Patient EHR Records -- Chapter 4: Predicting Medical Billing Codes from Clinical Notes -- Chapter 5: Extracting Structured Data from Receipt Images Using a Graph Convolutional Network -- Chapter 6: Handling Availability of Low-Training Data in Healthcare -- Chapter 7: Federated Learning and Healthcare. -- Chapter 8: Medical Imaging -- Chapter 9: Machines Have All the Answers, Except What's the Purpose of Life? -- Chapter 10: You Need an Audience Now.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Medical applications. -
Online resource:	https://doi.org/10.1007/978-1-4842-7086-8
ISBN:	9781484270868

AI for healthcare with Keras and Tensorflow 2.0 = design, develop, and deploy machine learning models using healthcare data /
Anshik.

AI for healthcare with Keras and Tensorflow 2.0design, develop, and deploy machine learning models using healthcare data /[electronic resource] :by Anshik. - Berkeley, CA :Apress :2021. - xvi, 381 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Healthcare Market: A Primer -- Chapter 2: Introduction and Setup -- Chapter 3: Predicting Hospital Readmission by Analyzing Patient EHR Records -- Chapter 4: Predicting Medical Billing Codes from Clinical Notes -- Chapter 5: Extracting Structured Data from Receipt Images Using a Graph Convolutional Network -- Chapter 6: Handling Availability of Low-Training Data in Healthcare -- Chapter 7: Federated Learning and Healthcare. -- Chapter 8: Medical Imaging -- Chapter 9: Machines Have All the Answers, Except What's the Purpose of Life? -- Chapter 10: You Need an Audience Now.

Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask. By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. You will: Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies Look at different problem areas within the healthcare industry and solve them in a code-first approach Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks Understand the industry and learn ML.

ISBN: 9781484270868

Standard No.: 10.1007/978-1-4842-7086-8doiSubjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / A47 2021

Dewey Class. No.: 610.28563

AI for healthcare with Keras and Tensorflow 2.0 = design, develop, and deploy machine learning models using healthcare data /
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505 0 $a Chapter 1: Healthcare Market: A Primer -- Chapter 2: Introduction and Setup -- Chapter 3: Predicting Hospital Readmission by Analyzing Patient EHR Records -- Chapter 4: Predicting Medical Billing Codes from Clinical Notes -- Chapter 5: Extracting Structured Data from Receipt Images Using a Graph Convolutional Network -- Chapter 6: Handling Availability of Low-Training Data in Healthcare -- Chapter 7: Federated Learning and Healthcare. -- Chapter 8: Medical Imaging -- Chapter 9: Machines Have All the Answers, Except What's the Purpose of Life? -- Chapter 10: You Need an Audience Now.
520 $a Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask. By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. You will: Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies Look at different problem areas within the healthcare industry and solve them in a code-first approach Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks Understand the industry and learn ML.
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