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Course Detail

Course Name Healthcare Analytics
Course Code 23DLS639
Semester 3
Credits 4

Course outcomes

CO1: Understand the basics of healthcare data analytics.
CO2: Gain knowledge about phenotyping algorithms.
CO3: Know the importance of clinical trials and prediction models.
CO4: To gain knowledge pervasive health analysis.

Introduction to Healthcare Data Analytics- Electronic Health Records–Components of EHR- Coding Systems- Benefits of EHR- Barrier to Adopting HER Challenges- Phenotyping Algorithms. Challenges in Healthcare Data Analysis, Acquisition Challenges, Pre-processing, Transformation , Social Media Analytics for Healthcare.
Advanced Data Analytics for Healthcare : Review of clinical trials , Prediction Models. Statistical Prediction Models, Alternative Clinical Prediction Models, Survival Models, Predictive Models for Integrating Clinical and Genomic Data, Data Analytics for Pervasive Health, Fraud Detection in Healthcare, Pharmaceutical Discoveries and Clinical Decision Support Systems.

Text / References books

  1. Chandan K. Reddy and Charu C Aggarwal, “Healthcare data analytics”, Taylor & Francis, 2015
  2. Hui Yang and Eva K. Lee, “Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, Wiley, 2016.

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