Anshik - AI for Healthcare with Keras and Tensorflow 2.0: Design, Develop, and Deploy Machine Learning Models Using Healthcare Data

This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.

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Like most readers of this book, your knowledge of healthcare is probably limited to your doctor visits and then reluctantly eating that bitter/sour medicine prescribed to you. But beyond that experience is a vast machinery of different stakeholders that have made it possible for us to receive the basic need of humankind: healthcare. If you want to understand how the healthcare system works and see how to apply AI to some of the most pressing problems in this space, read along.

Through this book, I will share my knowledge of the healthcare space gained from working with leaders from the top pharma companies in the world. Ill explain how they are using AI to automate, reorganize, and restructure different processes.

This book is a practitioners book. You will explore seven case studies covering multiple problems that often occur in healthcare analytics. I start by introducing you to the healthcare ecosystem so that the basic fundamentals are in place and then I offer a fun exercise of identifying whats hot in the industry right now using company descriptions.

Youll then move on to the problems often faced when working with EHR data (MIMIC-III). Youll use a multi-task setup to account for a heterogeneous patient population on downstream tasks like readmission prediction. Next, you will be introduced to the ICD code system, which is used for patient healthcare reimbursement, and youll leverage the mighty transformer models to identify them.

Chapter discusses the Graph Convolutional Network and how you can leverage its structure learning capability to read food receipts submitted by sales reps of a pharma company.

Chapter cover more nuanced aspects such as the availability of training data and privacy preservation. You will be introduced to semi-supervised learning using Snorkel and TensorFlows Federated API in detail.

Chapter is all about medical images and how to get the best of them using AI. You will learn about different image formats like DICOM and NIFTI, image modalities (X-Ray, CT, MRI, etc.) and different shapes (2-D, 3-D, and 4-D). You will solve two cases, image classification and segmentation using 2-D and 3-D images.

The last case study deals with how you can make your text alive by building a search engine on Covid research reports. You start by learning about different QnA systems and then you dive deeper by creating a closed-domain QnA system. You will learn about query paraphrasing, semantic retrieval, and reranking to fetch the right document on which you use a pretrained QnA model on SQUAD dataset.

Finally, you will deploy your model. In the last chapter, you will learn how web apps came into existence and how cloud technology is taking over. Docker-based app deployment using Flask is discussed.

Curiosity fuels the world

Anshik