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AI for Healthcare with Keras and Tensorflow 2.0 Anshik

AI for Healthcare with Keras and Tensorflow 2.0 By Anshik

AI for Healthcare with Keras and Tensorflow 2.0 by Anshik

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Intermediate-Advanced user level

AI for Healthcare with Keras and Tensorflow 2.0 Summary

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

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

About Anshik

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.

Table of Contents

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 concerns.Sub Topics: Introduction to federated learning and what it means for healthcare Hands on example on how to use the concepts of federated learningin one of your project Load and prepare an example decentralized datasets Design a federated learning architecture to predict diagnosisof inflammation in bladder. Learn about TensorFlow federated
Chapter 7: Medical ImagingChapter Goal: Complete end to end analysis of how to develop a deep -learning based medical diagnosis system using images. Learn about different kinds of image scans available like (cellular images, X-Ray scans etc.) . Also learn about the challenges such as accessibility of data, difference in image quality and how to address it, explainability etc. in disease detection via images.Sub Topics: What is medical imaging Different kinds of image analysis Deep learning based methods for image analysis Understanding how to deal with 2-D and 3-D images Solve image classification and segmentation problem Understand challenges like accessibility of data, image quality issues,explainability etc.
Chapter 8: Machine has all the Answers, Except What's the Purpose of Life.Chapter Goal: Introduction to concepts of a Question & Answering system.Comparative analysis of different Question and Answering architectures. Hands-on-Example of building your own Q&A system to ask and query questions over published medical papers on pubmed.Sub Topics: Review and understand various Question & Answering Techniques. Comparative analysis of different Question and Answeringarchitectures What is BERT architecture ? Using Bio-Bert architecture to train your own Q&A System
Chapter 9: You Need an Audience NowChapter Goal: Learned something from the book, excited to show it to theworld. In this chapter we are going to do exactly that, we are going to learn how to bring your models live and let the world interact with it. We will be building a Django app taking the Question Answering case study in point and also learning the basics of using docker for deployment.Sub Topics: Understand technologies like Streamlit, Flask and Django that can helpyou deploy your model depending upon the use case. What is docker and why should we dockerize our solutions. Building a production grade docker application. Django basics Using services like Heroku or Github SPAs to deploy your DjangoApp and bring it live.

Additional information

NLS9781484270851
9781484270851
1484270851
AI for Healthcare with Keras and Tensorflow 2.0: Design, Develop, and Deploy Machine Learning Models Using Healthcare Data by Anshik
Anshik
New
Paperback
APress
2021-06-26
381
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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