AI for Healthcare with Keras and Tensorflow 2.0

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.

What You Will Learn

• 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

Who This Book Is For

Data scientists and software developers interested in machine learning and its application in the healthcare industry

Table of Contents

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

About the Author

Anshik has a deep passion for building and shipping data science solutions that create great business value. He is currently working as a senior data scientist at ZS Associates and is a key member on the team developing core unstructured data science capabilities and products. He has worked across industries such as pharma, finance, and retail, with a focus on advanced analytics. Besides his day-to-day activities, which involve researching and developing AI solutions for client impact, he works with startups as a data science strategy consultant. Anshik holds a bachelor’s degree from Birla Institute of Technology & Science, Pilani. He is a regular speaker at AI and machine learning conferences. He enjoys trekking and cycling.