DISSERTATION - 2023/4

Module Overview

The data produced in biomedical and health informatics research covers a lot of different approaches and analyses. For example, comorbidity research, the environment and wellness, effect of pets on wellness, DNA sequence relations to health, messenger RNA/protein levels and disease. The modern techniques available mean that data acquired is huge and can be challenging to manipulate. Within this module we expect the student to develop a hypothesis-led or hypothesis-free research question, focus on and consider a specific data resource, and then either by means of manipulating the data, or through literature review, develop an understanding of disease, wellness, environmental health or some other issue in the form of an extended piece of written work. Students will build upon skills gained in taught modules. For example, in leveraging knowledge gained from the modules on Big Data in Biomedicine and Health, Introduction to Health Informatics and Digital Health, and Tutorials in Health Data Science, to scope the literature, understand how data were collected, define a relevant and novel research question, embed the work in current research, and create an analysis plan. Students will also use techniques covered by the Statistics and Modelling for Health Data, and the Machine Learning and AI modules, by applying these to answer their defined research question; as well as apply approaches covered by the Reporting and Data Visualisation module to best present their methodology and analysis results. Further, learning from the Stratified Medicine and Biomedical Data Analysis module will aid those students who choose a dissertation project that includes an element of analysis of biological and 'omic data. The dissertation should demonstrate that the student can understand how current evidence relates to a particular research question, and how their own work adds to this. Independent thinking is required with an ability to critique one's own work and that of others. The data derived using specific informatics methods, or the systematic review, will be written up in a form similar to a scientific paper. Students will be expected to be able to defend the findings presented in the dissertation as well as be aware of the implications of their work.

The dissertation project will provide students with an opportunity to demonstrate their research and presentation skills, by producing a piece of written scientific work. Students will further develop skills such as data analysis and information synthesis (enhancing their digital capabilities), critical thinking, problem solving, creative thinking, intellectual insight and argument construction, self-management, professional conduct and communication (contributing to self-assurance, resilience and resourcefulness); all of which are key skills to enhance the students future employability. Module aims: The aim of this module is for students to develop their focused data analysis skills, writing skills, abilities to stay within word limits, understand 'state-of-the-art' omics or digital health methods and write about them clearly. This will be achieved by providing students with an opportunity to pursue a single topic in depth and to demonstrate evidence of research ability at a Masters level. Students will choose from a range of dissertation titles offered by academic staff in the areas of health informatics, medical or clinical informatics, data sciences, veterinary bioinformatics, and biomedical informatics. Students may also define their own topic of interest for the dissertation, and identify a suitable supervisor to oversee the project. Students are encouraged to either carry out an original piece of research work through the manipulation of data within a health/bioinformatic dataset (e.g. some part of the UK Biobank data, Human Animal Bond Research Institute data) to generate new findings previously not seen with the data at hand; or to carry out a substantial survey of the literature on a particular topic. Students will gain an ability to assess data and write about it in a well-founded and compelling fashion. This module will provide the students with knowledge and experience of the multiple steps (e.g. experimental step, data analysis and interpretation) involved in writing a report or a scientific paper. In the Introduction and the Discussion, they will learn how to critically apprise research work of others.

Module provider

School of Health Sciences

Module Leader

WHETTON Tony (Biosciences)

Number of Credits: 60

ECTS Credits: 30

Framework: FHEQ Level 7

Module cap (Maximum number of students): 35

Module Availability

Semester 2

Prerequisites / Co-requisites

The compulsory (core) modules are required and need to be taken prior to this module.

Module content

The dissertation is the result of an expected 600 hours of work. Most of this is done individually by the student, in locating and reading relevant sources, working on the technical contribution that is the main part of the dissertation, and writing up the final report. Some time is also spent in regular discussions with the supervisor.

Assessment pattern

Alternative Assessment

NA

Assessment Strategy

The assessment strategy is designed to allow students to take the knowledge acquired in lectures and seminars plus all taught materials to address specific research questions in a dissertation or project set by a member of academic staff. The project will have clarity and goals devised by academic staff but can be adapted, extended, and further developed by the student. This will provide students with a clear framework in which to carry out a well-defined research project; as well as the structure to allow for continuous formative feedback to be of greatest value. The practicals attended by the student throughout the course will provide the ability for implementation with, and evaluation of a set of tools for real-world health care and biomedical data with the focus on the selection of appropriate online tools for data analysis. Then the writing will be to a prescription set by course organisers (number of words for Abstract, Introduction, Methods, Results, Discussion, Weaknesses in the Study will be defined).

Thus, the summative assessment for this module consists of marking submitted work. Formative assessment/feedback will be offered in the following ways.

• During first meeting after the student has read about the project where they will be asked to explain the concepts behind the work and their strategy for tackling the work (addressing learning outcome 1)


• Sample of Introduction text (~300 words) discussed with supervisor (addressing learning outcome 2)


• Results discussed with supervisor and feedback on way forward given (addressing learning outcomes 1, 2 and 3)


• Samples of Results and Discussion text (~800 words) discussed with supervisor (addresing learning outcomes 3, 4 and 5)

Module aims

• The aim of this module is for students to develop their focused data analysis skills, writing skills, abilities to stay within word limits, understand 'state-of-the-art' omics or digital health methods and write about them clearly. This will be achieved by providing students with an opportunity to pursue a single topic in depth and to demonstrate evidence of research ability at a Masters level.
  Students will choose from a range of dissertation titles offered by academic staff in the areas of health informatics, medical or clinical informatics, data sciences, veterinary bioinformatics, and biomedical informatics. Students may also define their own topic of interest for the dissertation, and identify a suitable supervisor to oversee the project. Students are encouraged to either carry out an original piece of research work through the manipulation of data within a health/bioinformatic dataset (e.g. some part of the UK Biobank data, Human Animal Bond Research Institute data) to generate new findings previously not seen with the data at hand; or to carry out a substantial survey of the literature on a particular topic.
  
• Students will gain an ability to assess data and write about it in a well-founded and compelling fashion. This module will provide the students with knowledge and experience of the multiple steps (e.g. experimental step, data analysis and interpretation) involved in writing a report or a scientific paper. In the Introduction and the Discussion, they will learn how to critically apprise research work of others.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to support students through their own research project, guiding them in addressing one of the many major opportunities that lie in analysis of huge epidemiological, health or clinical studies such as All of Us and UK Biobank.

The dissertation project will provide students with the opportunity to draw on research skills and expertise developed through earlier course modules; students will integrate what they have learnt throughout the course, by applying the knowledge and skillsets gained to a new research question or topic.

The dissertation project will focus on a self-directed learning, taking a Socratic approach to student guidance as the key method for learning and teaching. Students will have regular meetings with their supervisor to discuss progress with the dissertation and report writing. In practice, the process of undertaking this piece of written work will consist of several stages. First there will be an assessment of the study area by the student within the process of choosing the dissertation; and through an examination of existing literature. The student is given the opportunity to mould the project to their own interests though identification of gaps in current knowledge, discussion with the supervisor(s), and definition of a research question with achievable targets. Then, after agreement on the subject area, the breakdown into discrete sections of the work will occur via discussion between the student and supervisor. The student will submit an overall analysis plan for agreement, then begin the analysis work. The student will be permitted to submit 800 words of sample text for the supervisor for formative feedback, so that there is a check and guidance on direction and on quality. The student will be asked how they intend to refer to the literature and this will be checked by the supervisor. Students will be expected to document their use of data and analysis tools. Finally, students will present their dissertation is the form of a written report, ideally in the format of a scientific paper, that will be inclusive of an introduction/background, methods used, results, interpretation of the results, and drawn conclusion. This set of milestones does not deflect from the discussions of the work at hand that will take place in group fora and also in one-to-one meetings between student and supervisor.

The supervisor will discuss plagiarism, inclusion of figures, and other matters relating to originality to ensure no accidental additions of text or diagrams from the literature occurs.

A dissertation handbook has been prepared and will cover in detail the key milestones and marking schemes and other matters for the student.

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.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: BMSM039

Other information

This module is designed to allow students to develop knowledge, skills, and capabilities in the that address all five pillars inclusive of sustainability.

• Digital Capabilities: The dissertation projects will involve the use of learned techniques in data analytics and informatics, and lean heavily on the knowledge gained with regards to the use of such approaches as applicable to health and biomedicine. As such, students are expected to enhance their digital capabilities through practical and theoretical implementation.
• Resourcefulness and Resilience: The knowledge and experience gained through independent research projects, problem solving, as well as through embedding within research groups in the Faculty, will enhance students' resourcefulness and resilience. Opportunities to present their work, in written form, but also as part of group/lab meetings will further enhance the students' communication skills and self-assurance. Furthermore, there is opportunity for work emanating from dissertation project to be published within scientific journals or conferences; this will further support student development, self-confidence, as well as employability (see below).
• Employability: Through the carrying out of their research project, and the writing of the dissertation, students will gain valuable real-world experience that will significantly contribute to their employability. Students who successfully produce novel and innovating results will have the opportunity to publish these, as a self-contained publication, or as part of a wider project; such an achievement would add significantly to the students ability to obtain placements for further education, or in gaining employment across a wide range of sectors.
• Global and Cultural Capabilities: Conducting research in the health and biomedical domains comes with significant and complex ethical considerations; moreover, the application of data sciences and AI, to human-derived data, could be highly susceptible to bias, including ethnic, gender, and cultural. Through the dissertation project, students will be further made aware of these considerations, and asked to reflect on how these may be addressed within the context of their research. We will encourage students to commit to the safe and ethical innovation in data and AI and ensure students are fully compliant with all aspects of the appropriate guidelines and the law.
• Sustainability: Re-examination of large datasets that have been developed with a major carbon footprint is a key tenet of our approach to science on this course, and in the Faculty. This ethos is embedded in the dissertation. Supervisors will set dissertation projects that look to add to human knowledge from existing datasets. This educational experience is not going to have a major impact on resources as it will be focused on computer-based research, or literature review, with no laboratory work involved.

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.