Machine Learning in Healthcare (no coding required!)

Name: Machine Learning in Healthcare (no coding required!)
Rating: 4.2 (860 reviews)

Learn how to apply machine learning techniques in healthcare

Created byEddie Jay

Last updated 7/2024

English

What you'll learn

8 sections • 42 lectures • 2h 45m total length

Course Structure1:32
The slides can be downloaded at Lecture 4 Electronic Health Record section
You will find the dataset in the Assignment in Section6
Case study1:17
To make what you learn here concrete and actionable, we will use a case study - opioid dependency prediction at a clinic.
You will find the training dataset in the Assignment in Section6 of this course.

Steps to Predictive Modeling1:06
7 steps to predictive modeling/Machine learning
Planning the analysis3:57
A solid analytic plan ensures you ask the important questions upfront and keeps you on the right track, improving the efficiency and effectiveness of your analysis.
Getting Buy in for the analysis7:31
Your ability to convince others to work with you, on a project, is a key skill as an analyst. Remember, without end users, what you analyze is theoretical, pointless. Market yourself and your projects like a pro!
Data use considerations1:58
Why plan?
Why do we need buy in for the project?

5 key elements of health insurance products2:58
US Health System series
The 5 key elements of health plan design, that help you see and understand the whys behind each different design
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/
Intro to the Payors lines of business (health insurers)3:03
US Health System series
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/
Health data 101 - Health Insurance Claims2:09
Health insurance claims data are a major source of health care data. As long as the the medical services are paid by health insurers, you will see the claims. This is a relative well structured and complete source, although clinical detail can be lacking at times.
View the complete course + others here ==> https://www.udemy.com/user/yidingjiang/

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
1. Opioid abuse prediction case study
2. KNIME (a free machine learning software, no coding required!)
Assess model performance
Output presentation and implementation