TUTORIALS IN HEALTH DATA SCIENCE - 2023/4
Module code: BMSM036
Health and Biomedical Informatics is an ever-evolving field, and like other growing areas of scientific research, requires a constant surveillance of emerging methodologies and applications within the discipline. The purpose of this module is to provide students with an overview of the wide range of topics which fall within health data sciences and biomedical informatics. This will be achieved through a series of seminar lectures, including from a number of guest speakers, presenting some of the most influential work in the field. Students will take from these examples and develop their own conference-style presentations (both oral and in writing), on a current topic in the area, providing them with the skills and experience in scientific presentation.
This module will build directly on learning and knowledge gained in previous modules, such as methods in statistics and machine learning, Big Data resources in the health and biomedicine, digital health and health informatics.
School of Health Sciences
GEIFMAN Nophar (Health Sci.)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): 35
Overall student workload
Workshop Hours: 20
Independent Learning Hours: 100
Seminar Hours: 12
Tutorial Hours: 10
Guided Learning: 6
Captured Content: 2
Prerequisites / Co-requisites
The module will cover current topics in health data sciences and will include the following:
- Applications of data sciences in biomedical research
- Use of techniques in data sciences within clinical settings
- Advancements in AI and their use for health research and practice
Further the module will cover practical skills in scientific presentations, including:
- The dos and don’ts of poster presentations
- How to write a lay summary
- Preparing a scientific talk
|Assessment type||Unit of assessment||Weighting|
|Oral exam or presentation||Project Presentation||50|
|Coursework||Supporting presentation material (presentation notes/slides)||5|
|Coursework||A lay summary of the project presentation||5|
Pre-recorded presentation to replace live presentation; these could be submitted individually to replace group-work.
The assessment strategy is designed to allow students to demonstrate their acquired ability to critically assess the application of data sciences and informatics in the fields of health and biomedicine, pointing to the benefits and limitation of the chosen application, and through embedding their analysis within the wider research landscape (in the fields itself but also more widely). Through both the formative and summative assessment, students will demonstrate learnt skills in preparation of scientific presentation materials; while the summative assessment will also allow students to demonstrate their gained skills in formal oral presentation, collaborative team working and the ability to provide feedback to peers.
Thus, the summative assessment for this module consists of:
Coursework that will account for 50% of the final grade and will include:
• Poster (individual work) submitted as PDF file; 40% of final grade (addressing learning outcomes 2,3,4,5, and 6)
• A lay summary of the presented research (individual work), 500 words; 5% of final grade (addressing learning outcomes 1 and 6)
• Presentation slides + notes (group work); 5% of final grade (addressing learning outcomes 6 and 7)
The final presentation will account for 50% of the final grade and will include:
• Lecturer/module lead assessment; 40% of final grade (addressing learning outcomes 2,3,4,5,6, and 7)
• Peer scores and feedback; 10% of final grade (addressing learning outcomes 6 and 7)
Students will submit an outline (concept map) of their planned poster for formative assessment prior to submission of the final poster; feedback and guidance will be provided to ensure students are on the right track to produce their summative assessment (poster).
Students will receive written feedback from the module lead / lecturer on each of the assessment elements; this includes the poster outline (formative), the final poster, the presentation slides, and the lay summary. Verbal feedback will be provided on the final presentation itself as part of the follow-up discussion within the presentation session. Students will have the opportunity to discuss their work, and progress and be given verbal feedback during tutorial sessions.
The marking strategy will be clearly communicated with students at the start of the module.
- Introduce students to current topics in the field of data sciences, where applied in medicine and health, and animal health
- Demonstrate the range of health data science skills and research
- Provide students with the skills to prepare materials for a conference style presentation
- Equip students with skills in scientific presentation
- Review approaches of implementation of data sciences in the health, medicine and biology
- Provide students with skills for critical appraising and examination of a range of scientific work within the domain
|001||Familiarisation with a range of data sciences applications||K|
|002||Discuss of the benefits and limitations of novel approaches in data sciences, as applied to healthcare and biomedical research||CKT|
|003||Ability to critically review relevant publications, synthesise and critically evaluate cross-disciplinary knowledge||CPT|
|004||Ability to describe and assess use of data sciences in health and biomedicine disciplines||C|
|005||Develop an overview of the variety of research in the field and how it fits in the wider research landscape||CKT|
|007||Ability to produce written scientific summaries, create presentation materials (such as scientific poster) and give formal oral presentations covering ideas and concepts in data sciences to a mixture of audiences||PT|
|008||Ability to work collaboratively within a team, understanding different working styles and how to contribute to discussion and provide and respond to feedback of peers||KPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide students will the skill sets required to critically review, describe and analyse uses of data sciences in the domains of health, medicine and biology.
This unit will be taught in a seminar and project style, with small team projects assessed through conference-style reports. This unit will be delivered as a series of seminar lectures on cutting-edge topics in health data science skills, accompanied by scientific literature (key papers and book chapters) that will be made available online. We will focus on Problem-Based Learning, through the experience of solving an open-ended problem found in trigger material.
Conference-style presentations, will be developed in small groups (2-3 students), each focusing on a specific and current topic relevant to health data sciences. Students will choose a key (and recent) research publication, and could include additional supporting and related papers. Using these publications, the students will develop a conference-style presentation, that will be presented to the class and discussed with the wider group.
The learning and teaching methods therefore include:
- Seminars - these will introduce student to a range of current topics and research in the field
- Workshops – these will enable group-based work and development of ideas and concepts
- Participating in discussion
- Reading using lecturer’s guidance
- Researching and preparing final assessments
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: BMSM036
The MSc Health and Biomedical Informatics programme is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
- Digital Capabilities: This module will further develop the students’ digital capabilities by discussing how digital and data innovation is shaping the domains of health and medicine; and through further developing the students’ skills in using digital tools for scientific research and presentation.
- Resourcefulness and Resilience: The module will also provide students with experiences that will enhance their confidence in scientific presentation, as well as resourcefulness and self-assurance through working in small groups, problem-based learning, as well as peer-to-peer feedback.
- Global and Cultural Capabilities: Through introduction and understanding of the forefront of data sciences as applied in health and biomedicine, students will be encouraged to consider how this field is shaping the wider research and implementation landscape to impact and benefit society, both in the UK and more globally. This module will provide opportunities for students to engage with diverse and global perspectives, through guest speakers, as well as case studies from a range of countries, cultures and environments.
- Employability: The knowledge gained through this module as well as the skillsets gained through workshops and assessments, in particular in scientific presentation, will provide students with experience that will directly benefit their next careers progression and ability to take the next steps in the areas of data sciences and informatics, but also more widely.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.