MP4071 Healthcare Analytics Syllabus:
MP4071 Healthcare Analytics Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To impart the fundamental concepts of Healthcare data analytics
To give the knowledge about the Health care Data Sources.
To familiarize Advanced Data Analytics for Healthcare
To learn the Health IoT data analytics
To implement the Applications and Practical Systems for Healthcare.
UNIT I INTRODUCTION
Introduction- Healthcare Data Sources and Basic Analytics – Healthcare Data Sources : Electronic Health Records: Components of HER- Coding system- Biomedical Image Analysis: Biomedical Imaging Modalities- Object Detection- Image Segmentation- Image Registration- Feature Extraction- Mining of Sensor Data in Healthcare: Mining Sensor Data in Medical Informatics: Scope and Challenges- Sensor Data Mining Applications
UNIT II HEALTHCARE DATA SOURCES
Biomedical Signal Analysis: Types of Biomedical Signals- ECG Signal Analysis- Denoising of Signals- Multivariate Biomedical Signal Analysis- Cross-Correlation Analysis- Methods to Study Connectivity- Genomic Data Analysis for Personalized Medicine: Genomic Data Generation Methods and Standards for Genomic Data Analysis- Types of Computational Genomics Studies towards Personalized Medicine
UNIT III ADVANCED DATA ANALYTICS FOR HEALTHCARE
Basic Statistical Prediction Models- Alternative Clinical Prediction Models- Survival Models Evaluation and Validation- Temporal Data Mining for Healthcare Data: Association Analysis Temporal Pattern Mining- Sensor Data Analysis- Other Temporal Modeling Methods- Visual Analytics for Healthcare: Visual Analytics and Medical Data Visualization- Visual Analytics in Healthcare.
UNIT IV HEALTH IOT DATA ANALYTICS
Internet of things in the healthcare industry- IoT healthcare architecture- Characteristics of IoT health data- Health data analytics using Internet of things- Computational intelligence in Internet of things for future healthcare applications.
UNIT V APPLICATIONS AND PRACTICAL SYSTEMS FOR HEALTHCARE
Data Analytics for Pervasive Health: Supporting Infrastructure and Technology – Basic Analytic Techniques- Advanced Analytic Techniques- Applications – Fraud Detection in Healthcare- Data Analytics for Pharmaceutical Discoveries- Clinical Decision Support Systems.
COURSE OUTCOMES:
CO1: Describe the basics of healthcare data analytics.
CO2: Explain the Healthcare Data Sources.
CO3: Discuss the Advanced Data Analytics for Healthcare.
CO4: Express the Health IoT data analytics.
CO5: Apply the practical Systems for Healthcare.
TOTAL: 45 PERIODS
REFERENCES
1. Chandan K. Reddy , Charu C. Aggarwal, Healthcare Data Analytics 1st Edition, Kindle Edition, CRC press, 2020.
2. Sanjay Kumar Singh Ravi Shankar Singh Anil Kumar Pandey Udmale S.S. Ankit Chaudhary , IoT-Based Data Analytics for the Healthcare Industry Techniques and Applications 1st Edition, Elsevier, Academic Press
3. Prasant Kumar Pattnaik, Suneeta Mohanty (Editor), Satarupa Mohanty (Editor) Format: Kindle Edition, Smart Healthcare Analytics in IoT Enabled Environment 1st edition Kindle Edition, Springer Nature Switzerland AG 2020
4. Nilanjan Dey, Amira Ashour, Simon James Fong , Chintan Bhatt , Healthcare Data Analytics and Management 1st Edition, Elsevier, Academic Press 2018.
5. Sanket Shah, Healthcare Analytics: A Comprehensive Guide, Kindly Edition, 2020