AI for Healthcare with Keras and Tensorflow 2.0

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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 explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and related terminologies in simple terms. You then will move on to ML applications in healthcare using case studies with coding. This is followed by looking at datasets in healthcare-EHR data with multi-task deep learning (DL). Next, you will go over a neurolinguistic programming (NLP) case study covering transfer learning in NLP. You also will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. There is a chapter on medical imaging analysis showing you how to deal with 2D and 3D images. The concluding section shows you how to build a Q&A, system using a pre-trained transformer model. It also discusses Bio-Bert architecture to train your own Q&A, system. And, lastly, you go through an ML application made live with Docker using Django. By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and DL tools and techniques to the healthcare industry. What You Will LearnUnderstand the healthcare industryDesign, develop, train, validate, and deploy machine learning models on healthcare dataBe familiar with best practices for debugging and validating machine learning modelsKnow how transfer learning and federated learning differ Who This Book Is For Data scientists and software developers interested in machine learning and its application in the healthcare industry