AI for Healthcare with Keras and Tensorflow 2.0

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 techniq
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.