B.E Artificial Intelligence and Machine Learning AI for Healthcare Syllabus - Mumbai University
This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.
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Syllabus for AI for Healthcare
Module 1: Introduction
- 1.1 Overview of AI , ML and DL ,A Multifaceted Discipline, Applications of AI in Healthcare Prediction, Diagnosis, personalized treatment and behavior modification, drug discovery, followup care etc,
- 1.2 Realizing potential of AI in healthcare, Healthcare Data - Use Cases.
Module 2: AI, ML, Deep Learning and Data Mining Methods for Healthcare
- 2.1 Knowledge discovery and Data Mining, ML, Multi classifier Decision Fusion, Ensemble Learning, Meta-Learning and other Abstract Methods.
- 2.2 Evolutionary Algorithms, Illustrative Medical Application-Multiagent Infectious Disease Propagation and Outbreak Prediction, Automated Amblyopia Screening System etc.
- 2.3 Computational Intelligence Techniques, Deep Learning, Unsupervised learning, dimensionality reduction algorithms.
Module 3: Evaluating learning for Intelligence
- Model development and workflow, evaluation metrics, Parameters and Hyperparameters, Hyperparameter tuning algorithms, multivariate testing, Ethics of Intelligence.
Module 4: Natural Language Processing in Healthcare
- 4.1 NLP tasks in Medicine, Low-level NLP components, High level NLP components, NLP Methods.
- 4.2 Clinical NLP resources and Tools, NLP Applications in Healthcare. Model Interpretability using Explainable AI for NLP applications.
Module 5: Intelligent personal Health Record
- Introduction, Guided Search for Disease Information, Recommending SCA's. Recommending HHP's , Continuous User Monitoring.
Module 6: Future of Healthcare using AI 08
- Evidence based medicine, Personalized Medicine, Connected Medicine, Digital Health and Therapeutics, Conversational AI, Virtual and Augmented Reality, Blockchain for verifying supply chain, patient record access, Robot - Assisted Surgery, Smart Hospitals, Case Studies on use of AI and ML for Disease Risk Diagnosis from patient data, Augmented reality applications for Junior doctors. Blockchain for verifying supply chain, patient record access, Robot - Assisted Surgery, Smart Hospitals, Case Studies on use of AI and ML for Disease Risk Diagnosis from patient data, Augmented reality applications for Junior doctors. Total 39
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
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.