Introduction to Digital Health

MEDN67572 Introduction to Digital Health 15 Semester 2 Postgraduate Taught Level 6 Thomas Mee Unit coordinator School of Medical Sciences 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 <div> This unit will introduce the students to different programming languages and computational methods in order for the student to develop an understanding of its application to various oncology scenarios. This will include an overview of standard and modern methodologies used for medical models and software development, examples of current NHS/academic projects utilising computer programming and the challenges around data use within the example projects.Topics will include:•           Hardware configuration•           Networking•           Monte Carlo modelling•           Big data analysis•           Machine learning•           Biological modelling•           Software management•           Relational databases•           Legislation•           Security</div>   <div> This unit will introduce the students to different programming languages and computational methods in order for the student to develop an understanding of its application to various oncology scenarios. This will include an overview of standard and modern methodologies used for medical models and software development, examples of current NHS/academic projects utilising computer programming and the challenges around data use within the example projects.Topics will include:•           Hardware configuration•           Networking•           Monte Carlo modelling•           Big data analysis•           Machine learning•           Biological modelling•           Software management•           Relational databases•           Legislation•           Security</div>   Provide students with an insight into computational modelling methods and principles in general, as well as specific implementation of these in the NHS and radiotherapy landscape with a brief introduction to data requirements, information governance and data protection <ul> <li> Develop knowledge of key programming languages</li> <li> Understand the basics of Machine Learning, AI and Big Data</li> <li> Develop a basic knowledge of computational research areas within the oncology landscape</li> <li> Understand data protection principles and data issues within research</li> </ul> <ul> <li> Distinguish and evaluate different approaches to research programming and computational methods</li> <li> Program problems for computational research</li> </ul> <ul> <li> Develop logical thinking, planning and problem solving for research problems</li> <li> Write a basic computer program</li> </ul> <ul> <li> Develop computer programming skills that can be used in any situation</li> <li> Participate in group projects</li> <li> Gain an understanding of general programming methodologies and techniques</li> <li> Gain an understanding of information governance within the healthcare setting </li> </ul>   <div> <ul> <li> Classroom based teaching</li> <li> Interactive group based sessions</li> <li> Online learning</li> <li> Independent study</li> </ul> 0 Other 50% 3 Report 30% 9 Set exercise 20% PC based multiple choice examination Feedback is given at the end of each session or unitWritten feedback provided within 15 working days for all assessments N IPEM Guidance for Commissioning and QA of a Networked Radiotherapy Department (2006)Berg, A B. Markov Chain Monte Carlo Simulations and Their Statistical Analysis (2004)Marr, B. Big Data: Using Smart Big Data, Analystics and Metrics to Make Better Decisions and Improve Performance (2015)IPEM Physics in Medicine & Biology Special Edition. Volume 62, Number 11. Recent Progress in Applications of Computing to Radiotherapy (2017) Tutorials 35 0 115