Digital Health MSc - Curriculum

Python for Medical Students  

Prof. Noam Shomron, Mr. Yazeed Zoabi

The primary objective of this course is to establish a solid foundation in coding using the "Python" programming language. Our emphasis lies in fostering a skill set that encompasses Python's utility for data analysis, and its versatile applications within the clinical and research spheres. Throughout the course, our intention is for students to gain adeptness through practical engagements with medical data, effectively merging real-world scenarios from clinical practice. The students will understand the basic principles of a programming language, will be able to adapt simple functions to clinical questions, will know how to run clinical queries on databases and analyze them, and will learn how to use the Internet to independently learn new skills. An integrative graduation project will serve as a culmination of the acquired skills, allowing students to seamlessly apply their knowledge and abilities to a real-world context.

Big Data in Health Care  

Prof. Noam Shomron & 8400

This course provides an organized and comprehensive understanding of AI (Artificial Intelligence) processes and terminology in the context of big data analysis in healthcare. The course focuses on real-world applications of AI in healthcare, including case studies, and targets medical students, life sciences professionals, medical residents, and researchers who aim to implement AI-based solutions in their medical practice.

Course Syllabus: A. Introduction to Big Data Analysis in Medicine: Approaches to big data in healthcare, how to ask the right questions when approaching big data, Data processing in healthcare. B. Approaches to Big Data Analysis in Healthcare: Technical aspects of big data analysis, Using machine learning (ML) and AI in medicine, Evolution of data analytics in healthcare. C. AI and Machine Learning Algorithms for Real-World Applications: Exploratory data analysis (EDA) for clinical datasets, Observability and interpretability in AI and data analytics, Benefits and challenges of using ML models in clinical settings. D. Regulatory Approach to AI in Healthcare: Israeli regulatory approach to AI in healthcare, Ministry of Health guidelines and requirements. E. Final: Application and implementation of acquired knowledge, facilitated hands-on implementation of acquired knowledge, real-world case studies on clinical data, group work on implementing AI solutions in healthcare.

Data Science in Healthcare - From Knowledge to Practice, Challenges and Implications  

Prof. Noam Shomron

This hands-on course offers a deep dive into the practical applications of data science in healthcare and provides a comprehensive understanding of data science in healthcare. The program covers data management, relational and non-relational databases, supervised and unsupervised learning models, advanced data science techniques, and machine learning for clinical applications. It also delves into natural language processing (NLP), large language models (LLMs), generative AI, medical image analysis, and bioinformatics tools for healthcare research. Practical learning is facilitated through the Azure platform, with topics such as cloud computing, text analytics, SQL, and machine learning workspace resources. The course concludes with a focus on the ethical considerations of using these technologies in healthcare.