Clinical and Translational Research (Thesis): Digital Health Innovation (M.Sc.) (45 credits)
Offered by: Medicine (Faculty of Medicine and Health Sciences)
Degree: Master of Science
Program credit weight: 45
Program Description
The M.Sc. in Clinical and Translational Research; Digital Health Innovation focuses on the basics of clinical epidemiology, medical artificial intelligence, clinical innovation, and applied data science, including the use and generation of digitized health and social data using specialized software. Fundamentals of current AI applications in medicine, methods to employ big data in clinical tool development, mathematical principals underpinning digital health and big data, and design thinking methodology in clinical innovation. High-volume streams of clinical and health-related data from clinical systems, wearables and social media. The thesis must focus on digital health innovation in relation to clinical and translational research.
Required Courses (36 credits)
| Course | Title | Credits |
|---|---|---|
| EXMD 601 | Real World Applications of Data Science and Informatics. | 3 |
Real World Applications of Data Science and Informatics. Terms offered: Winter 2027 Training in practical applications of health care data science. | ||
| EXMD 634 | Quantitative Research Methods. | 3 |
Quantitative Research Methods. Terms offered: Fall 2026 Topics covered include: 1) An overview of common research designs based on examples from research currently undertaken in the Division of Experimental Medicine; 2) Types of data arising from these designs; 3) Basic methods for data analysis; and 4) Application of these methods to student research projects. | ||
| EXMD 650 | Health Informatics | 3 |
Health Informatics Terms offered: Fall 2026 Basic concepts in bio and health informatics, health informatics architecture and standards, ethics, health information infrastructure and management of health information systems, electronic health record systems, patient and consumer-oriented health information systems, telehealth, clinical decision-support systems, computers in health education, clinical research informatics and frameworks for evaluation. | ||
| EXMD 693 | Master's Thesis Research 4. | 12 |
Master's Thesis Research 4. Terms offered: Fall 2026, Winter 2027 Independent research work under the direction of the Thesis Supervisor and the Supervisory Committee. | ||
| EXMD 694 | Master's Thesis Research 5. | 12 |
Master's Thesis Research 5. Terms offered: Summer 2026, Fall 2026, Winter 2027 Independent research work under the direction of the Thesis Supervisor and the Supervisory Committee. | ||
| EXSU 500 | Fundamentals of AI in Medicine | 3 |
Fundamentals of AI in Medicine Terms offered: Fall 2026 Introduction to artificial intelligence (AI) applied to issues in medical diagnosis, therapy selection and learning from health data. Various AI methods, electronic medical records, and ethical/security concerns. Machine learning approaches including deep learning and reinforcement learning without delving too deeply into the technical details. | ||
Complementary Courses (9-10 credits)
3 credits from the following:
| Course | Title | Credits |
|---|---|---|
| EXMD 630 | Developing Digital Innovations for Health Impact. | 3 |
Developing Digital Innovations for Health Impact. Terms offered: Winter 2027 Advanced innovative thinking, knowledge and skills that will aid in the development of innovative digital health solutions. It will integrate mind mapping strategies, design thinking, and usability evaluations that form the core of developing and refining an innovative digital health solution aimed to solve a well-defined clinical/public health problem. | ||
| EXSU 620 | Surgical Innovation 1. | 3 |
Surgical Innovation 1. Terms offered: Fall 2026 The process of surgical innovation and acquisition of hands-on skills necessary to work within a multi-disciplinary team in the creation of a novel, need driven, and marketable prototype used in the care of the surgical patient. This is the first of a 3 part course introducing concepts and performing needs analyses. | ||
3-4 credits from the following:
| Course | Title | Credits |
|---|---|---|
| BMDE 503 | Biomedical Instrumentation. | 3 |
Biomedical Instrumentation. Terms offered: Fall 2026 The principles and practice of making biological measurements in the laboratory, including theory of linear systems, data sampling, computer interfaces and electronic circuit design. | ||
| BMDE 508 | Introduction to Micro and Nano-Bioengineering. | 3 |
Introduction to Micro and Nano-Bioengineering. Terms offered: Fall 2026 The micro and nanotechnologies that drive and support the miniaturization and parallelization of techniques for life sciences research, including different inventions, designs and engineering approaches that lead to new tools and methods for the life sciences - while transforming them - and help advance our knowledge of life. | ||
| BMDE 517 | Electromagnetic Technologies for Biomedicine | 3 |
Electromagnetic Technologies for Biomedicine Terms offered: Fall 2026 Introduction into concepts and characteristics of electromagnetic (EM) fields interacting with the body, from theory through practice. Electromagnetic properties of biological tissues, safety considerations, electrode- and antenna-based measurements, and existing and emerging medical technologies. | ||
| BMDE 520 | Machine Learning for Biomedical Data. | 3 |
Machine Learning for Biomedical Data. Terms offered: Fall 2026 Theoretical and practical course in machine learning applied to the expanding richness of biomedical data, including multidimensional biomedical measurements centring on high-resolution body imaging and whole-genome common variant genetics. | ||
| BMDE 655 | Biomedical Clinical Trials - Medical Devices. | 3 |
Biomedical Clinical Trials - Medical Devices. Terms offered: Winter 2027 This course will train biomedical engineers to understand the clinical and business aspects of transferring a medical device idea into a commercial product. This course provides an overview of the pre‐clinical and clinical testing of medical devices, clinical trials, reimbursement systems, market analysis, sales models, and business models, as pertaining to medical devices. This course will also cover the design of randomized trials, including statistical principles, hypothesis postulating, bias minimization, and randomization methods. | ||
| COMP 550 | Natural Language Processing. | 3 |
Natural Language Processing. Terms offered: Winter 2027 An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing. | ||
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: Fall 2026, Winter 2027 Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
| ECSE 551 | Machine Learning for Engineers. | 4 |
Machine Learning for Engineers. Terms offered: Fall 2026, Winter 2027 Introduction to machine learning: challenges and fundamental concepts. Supervised learning: Regression and Classification. Unsupervised learning. Curse of dimensionality: dimension reduction and feature selection. Error estimation and empirical validation. Emphasis on good methods and practices for deployment of real systems. | ||
| EXMD 651 | Statistical Models for Health Research. | 3 |
Statistical Models for Health Research. Terms offered: this course is not currently offered. Introduction to the concepts necessary for construction of statistical models relevant to clinical research, biomedical research and other areas of health research. Modern methods and software for estimating the parameters of these models and for drawing inferences and predictions from them. The models will be applied to data drawn from health research studies. The value and limitations of statistical models vs. machine learning models. | ||
or a 3-credit course at the 500 level or higher as approved by the Director.
Note: Thesis programs are 45 credits, but the credits in this program may minimally be exceeded in some cases due to course selection.