| CODE | CIS5228 | |||||||||
| TITLE | Computational Methods for Digital Health | |||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | |||||||||
| MQF LEVEL | 7 | |||||||||
| ECTS CREDITS | 5 | |||||||||
| DEPARTMENT | Computer Information Systems | |||||||||
| DESCRIPTION | Digital technologies are constantly evolving and finding new applications in healthcare while, at the same time, new technologies emerge. This study-unit is meant to give student who choose the ICT stream in the M.Sc. Digital Health programme a strong foundation in the theory of the main IT technologies and tools used in digital health as well as some practical implementation experience. Study-unit Aims: Introduce the students to the concepts and theory behind modern digital health systems including: Data analytics, data handling, principles of data science, machine learning algorithms, Blockchain, data visualization, simulations (including pandemic simulation) and medical imaging techniques. Smart and wearable devices, patient monitoring systems, and mobile healthcare applications will be presented as use cases. Students will also be exposed to the main AI and machine learning algorithms used for predictive inference in modern healthcare and prevention. The students will learn how to make the best choice of machine learning algorithm (given a specific problem), how to prepare the date (including data cleansing, anonymization, sanitation, and feature selection, as well how to compare the results from different models.The programming language of choice for creating software artifacts will be Python. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Describe the challenges involved in the design and implementation of complex digital health systems; - Describe, and evaluate, the various components of modern digital health systems; - Describe, and implement, the main machine learning algorithms and inference methods used in the implementation of modern digital health systems; - Appreciate the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.; - Evaluate the strengths and weaknesses of many popular machine learning approaches; - Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning. 2. Skills By the end of the study-unit the student will be able to: - Analyse, and relate critically to, different sources of information, datasets and data processes; and apply these to structure and formulate data-driven reasoning; - Develop relevant skills in data management and analytics in healthcare; - Develop the ability to design or apply, machine learning algorithms of moderate complexity to solve, or approximate, problems in digital health; - Carry out data visualization and formal inference on healthcare data and be able to interpret the results; - In addition to performing data exploratory and inferential procedures, students can fit complex machine models to training data in healthcare; - Communicate on issues, analyses and conclusions related to data-driven research and development in healthcare - both with specialists and with the general public. Main Text/s and any supplementary readings: Main Texts: - Digital Healthcare: The Essential Guide, Paperback, by Ruth Chambers (Author), Marc Schmid (Author), Jayne Birch-Jones (Author), ISBN-13 : 978-1910303061 Publisher: Otmoor Publishing Ltd (30 Jun. 2016). - Healthcare Digital Transformation: How Consumerism, Technology and Pandemic are Accelerating the Future (HIMSS Book Series) Hardcover, by Edward W. Marx (Author), Paddy Padmanabhan (Author) ISBN-13 : 978-0367476571 Publisher: Productivity Press (3 Aug. 2020). Supplementary Text: - AI in Healthcare: Theory to Application: A commentary about the progress form conception to implementation, Paperback, by Dr Sandeep Reddy (Author) ISBN-13 : 978-1080499892 Publisher: Independently published (14 July 2019) . Other sources: Slides and notes available on the VLE. Various papers. |
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| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | |||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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