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Course Detail

Course Name Biomedical data acquisition & management
Course Code 24AIM302
Program B.Tech. Artificial Intelligence (AI) and Data Science (Medical Engineering)
Semester V - Micro-credential courses: Set 4
Credits 4

Syllabus

Unit 1

Introduction to Biomedical Data: Principles of biomedical sensors, instruments, and real-time signal processing. Advanced sensor technologies (wearables, implantables) for continuous monitoring.

Unit 2

Data Management in Biomedical Engineering: Health information systems, Electronic Health Records (EHR), and big data analytics. Security, privacy, and ethical considerations in managing patient information.

Unit 3

Biomedical Data Integration and Analytics: Techniques for integrating diverse biomedical data sources. Data warehouses, analytics, machine learning for predictive modeling. Patient-centric data integration, clinical trials, and research data management.

Unit 4

Emerging Trends and Applications: IoT for real-time biomedical data monitoring. Big data challenges and opportunities in biomedical engineering. Medical imaging data management and integration. Blockchain for data security, telehealth, and ethical considerations in biomedical data.

Course Objectives and Outcomes

Course Objectives:

  1. To introduce students to the fundamental biomedical data acquisition and management
  2. To enable students to appreciate and apply the connection between pertinent technologies and real-world medical problems.
  3. To expose students to the wide range of applications using analytics and informed decision making.
  4. To equip students with advanced skills in trends like IoT, which are highly valued in healthcare practices.

Course Outcomes:

After completing this course, students should be able to
CO1: Apply knowledge to interpret data from sensors and instruments, using mathematical concepts.
CO2: Implement health information systems.
CO3: Develop skills in integrating biomedical data, creating warehouses, and using analytics for informed decision-making.
CO4: Evaluate trends like IoT and navigate big data challenges in healthcare practices.

CO-PO mapping

CO/PO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

PSO3

CO1

2

–

–

–

2

–

–

1

2

2

–

2

3

2

2

CO2

2

–

–

–

3

–

–

1

2

2

–

2

3

3

2

CO3

3

3

–

–

3

–

–

1

2

2

–

2

3

2

2

CO4

3

3

–

–

3

–

–

1

2

2

–

2

3

2

2

Textbooks / References

  1. Edward H. Shortliffe, James J. Cimino, “Biomedical Informatics: Computer Applications in Health Care and Biomedicine,” 4th Edition, Springer, 2014.
  2. Robert E. Hoyt, Ann K. Yoshihashi, “Health Informatics: Practical Guide for Healthcare and Information Technology Professionals,” 7th Edition, Lulu, 2021.
  3. Arnon Cohen, Israel Gannot, “Biomedical Signal Processing and Signal Modeling,” CRC Press, 2022.
  4. Sergio Manzi, Riccardo Rizzo, “Big Data in Healthcare: Statistical Analysis of Features in Selected Datasets,” SpringerBriefs in Pharmaceutical Science & Drug Development, Springer, 2020.

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