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MODULE DESCRIPTOR
Module Title
Multi-modal Data Science for Digital Health
Reference CMM561 Version 1
Created November 2022 SCQF Level SCQF 11
Approved January 2023 SCQF Points 15
Amended ECTS Points 7.5

Aims of Module
To provide students with an introduction to the theoretical knowledge to critically appraise and develop decision-support systems for digital health, and to empower practical skills and business understanding of the domain by deep diving into individual use cases which exemplify different aspects of data analysis on multi-modal health data.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Appraise different types of data science strategies in the context of the digital health domain and its challenges.
2 Demonstrate an understanding of machine learning techniques to develop solutions for multi-modal healthcare problems.
3 Evaluate the performance of data science techniques within a given business context.
4 Visualise and explain the outcomes of data science pipelines for different stakeholders in digital health.

Indicative Module Content
Basic data modelling: identification and selection of features from business data, application of a data science pipeline, evaluation and selection of methodologies. Data analysis techniques: computer vision, natural language processing/generation, time-series analysis, suitable algorithms for all cases. Context of decision-support systems in digital health domain: typical use-cases and data, evaluating decision-support systems and their outcomes. Developing decision-support systems: types of decision-support system, development strategies, alternatives.

Module Delivery
This module uses guided study (via lectures, tutorials and other learning materials delivered through the VLE), practical lab exercises and personal study. Key concepts are introduced and illustrated through online lectures. Student understanding further enhanced through the use of online lab tutorials. Directed learning is performed during flipped classroom sessions to discuss lecture/lab content.

Indicative Student Workload Full Time Part Time
Contact Hours N/A 25
Non-Contact Hours N/A 125
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL N/A 150
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4
Description: Portfolio assessment (comprising group-work as part of a Bring-Your-Own-Project team and individual assessment of completed exercises).

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in Component 1.
B The student needs to achieve a B in Component 1.
C The student needs to achieve a C in Component 1.
D The student needs to achieve a D in Component 1.
E The student needs to achieve an E in Component 1.
F The student needs to achieve an F in Component 1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 Aurelien Geron 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
2 Lupton, Deborah. Digital health: critical and cross-disciplinary perspectives. Routledge, 2017.
3 Various Eds,. Digital Health, [Journal]. Sage
4 Rivas, H. and Wac, K. [Eds], Digital Health: Scaling Healthcare to the World. Springer, 2018.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781