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30-Day Money-Back Guarantee
Business Business Analytics & Intelligence Machine Learning

Machine Learning in Healthcare (no coding required!)

Learn how to apply machine learning techniques in healthcare
Rating: 4.4 out of 54.4 (60 ratings)
483 students
Created by Eddie Jay
Last updated 5/2020
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Requirements

  • Basic knowledge of health care data
  • Knowledge of basic statistical concepts

Description

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:

  1. Health Data (sources, types, features, error handling)

  2. Logistics of machine learning

  3. What predictive model features are, and how to create them

  4. A statistical primer, highlighting key machine learning models and concepts

  5. 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!)

  6. Assess model performance

  7. Output presentation and implementation

Who this course is for:

  • Beginner to Intermediate analysts of health data
  • Public health/Epidemiology/Bioinformatics analysts
  • Actuarial/Statistical analysts

Course content

7 sections • 39 lectures • 2h 37m total length

  • Preview01:32
  • Preview01:17

  • Preview02:09
  • Electronic health records
    02:19
  • Research reports
    02:57
  • Public health
    02:17
  • Wearables
    02:40
  • Insurance Claims
    1 question
  • What are strengths of EHR data?
    1 question
  • Key takeaways from sources of health data session
    1 question
  • Structured data
    03:11
  • Data structure
    1 question
  • Unstructured data
    02:15
  • Preview04:17
  • Features of data - Hierarchical Drugs
    02:36
  • Features of data - Hierarchical Procedures
    03:51
  • Features of data - Hierarchical LOINCs and other
    02:23
  • Disease Etiology, Chronology, Supply vs Demand
    04:33
  • Why understanding of medicine and medical practices enhance analyses?
    1 question
  • Errors and error handling
    04:22
  • Correcting errors in health data
    1 question

  • Steps to Predictive Modeling
    01:06
  • Planning the analysis
    03:57
  • Getting Buy in for the analysis
    07:31
  • Data use considerations
    01:58
  • Why plan?
    1 question
  • Why do we need buy in for the project?
    1 question

  • Initial exploration of the data
    02:07
  • Data preparation
    03:44
  • Feature engineering
    12:49
  • On features
    1 question
  • From raw data to training dataset
    07:47

  • Preview00:58
  • Logistic regression - intro
    04:29
  • Logistic regression Pros and Cons
    05:46
  • Bias Variance trade off
    05:38
  • Bias Variance
    1 question
  • Decision tree
    03:20
  • When to use which?
    1 question
  • Random forest
    04:20
  • Neural net
    04:10

  • Intro to KNIME
    05:44
  • Model training in KNIME
    12:24
  • Build your own
    1 question

  • Preview01:17
  • Sensitivity vs Specificity
    03:28
  • On sensitivity and specificity
    1 question
  • Cross validation in KNIME
    04:38
  • Sensitivity/Scenario testing
    04:40
  • Presenting the results
    04:04
  • Implementation and Future improvements
    02:09
  • Practice practice practice!
    01:04

Instructor

Eddie Jay
Health Analytics Guru / Actuary
Eddie Jay
  • 4.4 Instructor Rating
  • 308 Reviews
  • 1,797 Students
  • 3 Courses

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.

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