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Working in a hands-on learning environment, led by our Healthcare expert instructor, students will learn about and explore: Perform healthcare analytics with Python and SQL.Build predictive models on real healthcare data with pandas and scikit-learn.Use analytics to improve healthcare performance.

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Last Updated

January 19, 2021

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Certification

Course Description

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practicing doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes.

This course is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed.

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Course Curriculum

    • What is healthcare analytics? 00:00:00
    • Foundations of healthcare analytics 00:00:00
    • History of healthcare analytics 00:00:00
    • Examples of healthcare analytics 00:00:00
    • Exploring the software 00:00:00
    • Healthcare delivery in the US 00:00:00
    • Patient data – the journey from patient to computer 00:00:00
    • Standardized clinical codesets 00:00:00
    • Breaking down healthcare analytics 00:00:00
    • Model frameworks for medical decision making 00:00:00
    • Machine learning pipeline 00:00:00
    • Introduction to databases 00:00:00
    • Data engineering with SQL – an example case 00:00:00
    • Case details – predicting mortality for a cardiology practice 00:00:00
    • Starting an SQLite session 00:00:00
    • Data engineering, one table at a time with SQL 00:00:00
    • Variables and types 00:00:00
    • Data structures and containers 00:00:00
    • Programming in Python – an illustrative example 00:00:00
    • Introduction to pandas 00:00:00
    • Introduction to scikit-learn 00:00:00
    • Additional analytics libraries 00:00:00
    • Introduction to healthcare measures 00:00:00
    • US Medicare value-based programs 00:00:00
    • The Hospital Value-Based Purchasing (HVBP) program 00:00:00
    • The Hospital Readmission Reduction (HRR) program 00:00:00
    • The Hospital-Acquired Conditions (HAC) program 00:00:00
    • The End-Stage Renal Disease (ESRD) quality incentive program 00:00:00
    • The Skilled Nursing Facility Value-Based Program (SNFVBP) 00:00:00
    • The Home Health Value-Based Program (HHVBP) 00:00:00
    • The Merit-Based Incentive Payment System (MIPS) 00:00:00
    • Other value-based programs 00:00:00
    • Comparing dialysis facilities using Python 00:00:00
    • Comparing hospitals 00:00:00
    • Introduction to predictive analytics in healthcare 00:00:00
    • Our modeling task – predicting discharge statuses for ED patients 00:00:00
    • Obtaining the dataset 00:00:00
    • Starting a Jupyter session 00:00:00
    • Importing the dataset 00:00:00
    • Making the response variable 00:00:00
    • Splitting the data into train and test sets 00:00:00
    • Preprocessing the predictor variables 00:00:00
    • Final preprocessing steps 00:00:00
    • Building the models 00:00:00
    • Using the models to make predictions 00:00:00
    • Improving our models 00:00:00
    • Predictive healthcare analytics – state of the art 00:00:00
    • Overall cardiovascular risk 00:00:00
    • Congestive heart failure 00:00:00
    • Cancer 00:00:00
    • Readmission prediction 00:00:00
    • Other conditions and events 00:00:00
    • Healthcare analytics and the internet 00:00:00
    • Healthcare and deep learning 00:00:00
    • Obstacles, ethical issues, and limitations 00:00:00

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