Machine Learning in Healthcare (no coding required!)
What you'll learn
- Health data 101
- How to plan the analysis and to get buy in
- What you should know about health data for predictive modeling purposes
- What predictive model features are, and how to create them
- Statistical model primer
- How to build predictive models: step by step guide: using case study
- How to assess model performance
- Basic knowledge of health care data
- Knowledge of basic statistical concepts
This course will teach you how to work with health data, using machine learning models to find actionable insights.
Through a step-by-step guided case study, you will learn practical skills that you can apply immediately!
We will use a case study: Opioid Abuse Prediction for a clinic
Topics we will cover:
Health Data (sources, types, features, error handling)
Logistics of machine learning
What predictive model features are, and how to create them
A statistical primer, highlighting key machine learning models and concepts
Build a decision tree, logistic regression and random forest through
Opioid abuse prediction case study
KNIME (a free machine learning software, no coding required!)
Assess model performance
Output presentation and implementation
Who this course is for:
- Beginner to Intermediate analysts of health data
- Public health/Epidemiology/Bioinformatics analysts
- Actuarial/Statistical analysts
I have worked in the data rich healthcare space for over 17 years.
I'm passionate about pragmatically utilizing health data to find actionable insights, thereby improving patient care.
I've worked for a variety of health care organizations including insurers, hospitals, pharmaceutical companies and software startups.
I know how to get value out of healthcare data.