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Introduction to Healthcare Analytics

Who Should Attend?

PERSONNELS involve in HEALTHCARE ANALYTICS

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Duration: 4 Days

The future of healthcare will be driven by digital transformation and data analysis. Healthcare data is the most complex of any industry. Healthcare analytics is the collection and analysis of data in the healthcare industry in order to gain insights and support decision-making. From key areas like medical costs, clinical data, patient behavior, and pharmaceuticals, healthcare analytics can be used on both macro and micro levels to effectively streamline operations, improve patient care, and lower overall costs. This course will introduce healthcare analytics solution that impact the healthcare industry with practical hands-on on use cases.

  • Explore and prepare data used in healthcare organization
  • Understand statistical concepts in healthcare analytics
  • Apply machine learning approaches to extract useful information from healthcare data
  • Construct predictive model using healthcare data
  • Interpret healthcare data to generate analytical insights
  1. Digital Transformation in Healthcare
    • Value-driven Healthcare System
    • Technology Enabled Clinical Care
    • Modern Patient Management
    • Transforming Healthcare with Analytics
  2. Data Management and Visualization
    • Data and Variables
    • Data Cleaning – Data Errors, Missing Values and Outliers
    • Data Exploration and Visualization
  3. Introduction to Machine Leaning
    • Overview of Machine Learning
    • Unsupervised Learning: Clustering & Association
    • Supervised Learning: Regression & Classification
  4. Unsupervised Learning in Healthcare
    • Application of Unsupervised Learning in Healthcare
    • Clustering Analysis
    • Association Rules
  5. Supervised Learning in Healthcare
    • Application of Supervised Learning in Healthcare
    • Prediction Accuracy vs Model Interpretability
    • Data Validation
    • Regression vs Classification
  6. Regression Analysis
    • Simple Linear Regression
    • Assessing Fit of Linear Regression
    • Multiple Linear Regression
    • Dummy Variable
  7. Classification
    • Validation and Performance
    • Logistics Regression
    • Naïve Bayes
    • Decision Tree
    • Neural Network
    • Support Vector Machine
Case Study
  1. Clustering Analysis
    • Healthcare claims data: A study of end-stage renal disease patient
  2. Association Rules
    • Medical health records: Discover correlation between diseases, diseases and symptoms, diseases and medicines
  3. Regression Analysis
    • Predicting hospital length of stay: Application to emergency department
  4. Classification
    • Chronic kidney disease prediction

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