B.E Artificial Intelligence and Machine Learning AI for Healthcare Lab Syllabus - Mumbai University

Module 1: Collect, Clean, Integrate and Transform Healthcare Data based on specific disease.

Module 2: Perform Exploratory data analysis of Healthcare Data.

Module 3: AI for medical diagnosis based on MRI/X-ray data.

Module 4: AI for medical prognosis .

Module 5: Natural language Entity Extraction from medical reports.

Module 6: Predict disease risk from Patient data.

Module 7: Medical Reviews Analysis from social media data.

Module 8: Explainable AI in healthcare for model interpretation.

Module 9

• Mini Project-Design and implement innovative web/mobile based AI application using Healthcare Data. (this needs to be implemented in group of 3-4 students )

Module 10: Documentation and Presentation of Mini Project.

Textbooks

• 1 Arjun Panesar, "Machine Learning and AI for Healthcare”, A Press.
• 2 Arvin Agah, "Medical applications of Artificial Systems ", CRC Press
• 1 Erik R. Ranschaert Sergey Morozov Paul R. Algra, “Artificial Intelligence in medical Imaging- Opportunities, Applications and Risks”, Springer
• 2 Sergio Consoli Diego Reforgiato Recupero Milan Petković,“Data Science for Healthcare Methodologies and Applications”, Springer
• 3 Dac-Nhuong Le, Chung Van Le, Jolanda G. Tromp, Gia Nhu Nguyen, “Emerging technologies for health and medicine”, Wiley.
• 4 Ton J. Cleophas • Aeilko H. Zwinderman, “Machine Learning in Medicine- Complete Overview”, Springer
• 1 https://www.coursera.org/learn/introduction-tensorflow?specialization=tensorflow-in-practice
• 2 https://www.coursera.org/learn/convolutional-neural-networks-tensorflow?specialization=tensorflo w- in practice
• 3 https://datarade.ai/data-categories/electronic-health-record-ehr-data
• 4 https://www.cms.gov/Medicare/E-Health/EHealthRecords
• 5 https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction?specialization=te nsorflow-in-practice