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Home » Technology » Big Data & Data Science » Healthcare Analytics Made Simple

Course Skill Level:

Foundational

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    HCANMSL21E09

Who should attend & recommended skills:

Those experienced in Python with basic IT & Linux skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who are intending to add a touch of data analytics to your healthcare systems and get insightful outcomes, such as using Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.
  • Skill-level: Foundation-level Healthcare Analytics Made Simple for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

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.

Skills acquired & topics covered

  • 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
  • Gain valuable insight into healthcare incentives, finances, and legislation
  • Discover the connection between machine learning and healthcare processes
  • Use SQL and Python to analyze data
  • Measure healthcare quality and provider performance
  • Identify features and attributes to build successful healthcare models
  • Build predictive models using real-world healthcare data
  • Become an expert in predictive modeling with structured clinical data
  • See what lies ahead for healthcare analytics

Course breakdown / modules

  • What is healthcare analytics?
  • Foundations of healthcare analytics
  • History of healthcare analytics
  • Examples of healthcare analytics
  • Exploring the software

  • Healthcare delivery in the US
  • Patient data – the journey from patient to computer
  • Standardized clinical codesets
  • Breaking down healthcare analytics

  • Model frameworks for medical decision making
  • Machine learning pipeline

  • Introduction to databases
  • Data engineering with SQL – an example case
  • Case details – predicting mortality for a cardiology practice
  • Starting an SQLite session
  • Data engineering, one table at a time with SQL

  • Variables and types
  • Data structures and containers
  • Programming in Python – an illustrative example
  • Introduction to pandas
  • Introduction to scikit-learn
  • Additional analytics libraries

  • Introduction to healthcare measures
  • US Medicare value-based programs
  • The Hospital Value-Based Purchasing (HVBP) program
  • The Hospital Readmission Reduction (HRR) program
  • The Hospital-Acquired Conditions (HAC) program
  • The End-Stage Renal Disease (ESRD) quality incentive program
  • The Skilled Nursing Facility Value-Based Program (SNFVBP)
  • The Home Health Value-Based Program (HHVBP)
  • The Merit-Based Incentive Payment System (MIPS)
  • Other value-based programs
  • Comparing dialysis facilities using Python
  • Comparing hospitals

  • Introduction to predictive analytics in healthcare
  • Our modeling task – predicting discharge statuses for ED patients
  • Obtaining the dataset
  • Starting a Jupyter session
  • Importing the dataset
  • Making the response variable
  • Splitting the data into train and test sets
  • Preprocessing the predictor variables
  • Final preprocessing steps
  • Building the models
  • Using the models to make predictions
  • Improving our models

  • Predictive healthcare analytics – state of the art
  • Overall cardiovascular risk
  • Congestive heart failure
  • Cancer
  • Readmission prediction
  • Other conditions and events

  • Healthcare analytics and the internet
  • Healthcare and deep learning
  • Obstacles, ethical issues, and limitations

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