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AI for Healthcare with Keras and Tensorflow 2.0
Design, Develop, and Deploy Machine Learning Models Using Healthcare Dataav Anshik400
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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 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
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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.
Chapter 1: Healthcare Market: A PrimerChapter Goal: Know how sub-markets like pharmaceutical, medicaltechnology, and hospital come together to form the healthcare ecosystem. Learn on how digital and mobile are shaping and reforming traditional health. With technology available and permissible to large masses via internet things like telehealth have become a norm. Also, what kind ofproblems are being solved at industry level and at various startups.Sub Topics:Healthcare Marketplace Overview Map of how different stakeholder comes together to form the system Medicare Overview Paying Doctors Healthcare CostsEmerging Trends Changing role of consumer in healthcare Future of Healthcare Payments Quality of Healthcare DeliveryIndustry 4.0 and Healthcare Chapter 2: Multi Task Deep Learning To Predict Hospital Re-admissionsChapter Goal: A real world case study showing how re-admissions whichcosts billions of dollars to the US healthcare system can be addressed. We will be using EHR data to cluster patients on their baseline characteristics and clinical factors and correlate with their readmission rates.Sub Topics: Introduction to EHR data. Exploring MIMIC III datasets Establishing a baseline model to assess re-admission rates usingensemble of classification models with handling class imbalance. Using auto-encoder to create a distributed representation of features. Clustering patients Analyzing readmission rate based on clusters. Comparative analysis between baseline and deep learning basedmodel. Chapter 3: Predict Medical Billing Codes from Clinical NotesChapter Goal: Clinical notes contain information on prescribed proceduresand diagnosis from doctors and are used for accurate billings in the current medical system, but these are not readily available. One has to extract them manually for the process to be carried out seamlessly. We are attempting to solve this problem using a classification model using the MIMIC III datasets introduced above.Sub Topics: Introduction to case study data. Learn about transfer learning in NLP by fine-tuning the BERT modelfor your task. Using various attention based sequence modelling architectures likeLSTM and transformers to predict medical billing codes. Chapter 4: Extracting Structured Data from Receipt ImagesChapter Goal: Just like any other sales job, the sales rep of a Pharma firm isalways on the field. While being on the field lots of receipts get generated for reimbursement on food and travel. It becomes difficult to keep track of bills which don't follow company guidelines. In this case study we will explore how to extract information from receipt images and structure various information from it.Sub Topics: Introduction to information extraction through Images. Exploring receipt data Using graph CNN to extract information What is a graph convolutional architecture How is it different from traditional convolutional layers Applications Hands on example to demonstrate training of a graph CNN Exploring recent trends in extracting information from templatedocuments. Chapter 5: Handle Availability of Low-Training Data in HealthcareChapter Goal: Availability of training data has limited the use of advancedmodels and general interest for problems in the healthcaredomain. Get introduced to weak supervision techniques that canbe used to handle low training data. Also learn about upcominglibraries (like Snorkel and Astron) and research in this field.Sub Topics: Explore weak supervision learning using Snorkel and Astron Learn to create label functions Hands on experimentation with a simple classification problem onapplication of concepts from weak supervised learning Chapter 6: Federated Learning and HealthcareChapter Goal: Federated learning enables distributed machine learning inwhich machine learning models train on decentralized data.This is deemed as the future of ML models as sharing patientlevel data becomes more difficult for organizations due toprivacy and security concern