This course introduces the essential biological knowledge to students with non-biology background in order for them to understand the basic concepts of precision medicine. It uses cancer as a model disease to illustrate how precision medicine can provide better, individually tailored healthcare solutions to patients. The course introduces the methods of discovering causes of cancer, the techniques to perform accurate diagnoses, the mechanisms of cancer therapies, and the different strategies for cancer treatment.
This course introduces the knowledge in digital medicine, technologies, and the associated big data and analysis to students with non-biology background. Digital medicine is the convergence of digital technologies (including genomic technologies) with medicine, living, and society to enhance the efficiency of medicine delivery and make medicines more personalized and precise. This course covers high-throughput (HT) genotyping, HT phenotyping, artificial intelligence, big data analysis, public database, proteomics and other omics. The goal of this course is to develop students’ understanding of digital medicine and required analytical skills for the exciting possibilities towards creating a new paradigm of precision medicine.
Healthcare analytics is one of the fastest growing industries in our economy. Healthcare analytics allows for the examination of patterns in various healthcare data in order to determine how clinical care can be improved. This course introduces the essential elements of healthcare analytics to students with non-healthcare background, and provides them with an overview of population health informatics, clinical informatics, imaging informatics, mobile healthcare and their contributions to precision medicine. The goal of this course is to develop students’ understanding of healthcare analytics and analytical skills to comprehend healthcare data.
This course introduces the concept of artificial intelligence (AI) in medicine, the associated big data analysis, and analytic programming. Medical AI is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. AI is currently transforming healthcare delivery and is augmenting doctors’ roles in medicine. This course covers analytic programming (predominately in R), big data analysis, and application for AI in medicine. The goal of this course is to develop students’ understanding and to confer hands-on analytical skills for AI in medicine on them.
An individual project provides the opportunity to plan and execute a significant project of research, investigation or development, and to integrate learning and put the techniques learnt throughout the master programme into practice under an academic supervision. Using large datasets from academia, hospitals, industry or government, students carry out high-level coordinated academic and practical work to solve a real-world problem in the field of biomedical sciences, medicine or healthcare, including collecting and processing real data, designing and implementing data science methods and tools, and applying, evaluating and critically assessing data analysis, visualization and prediction techniques to solve the real problem.