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COMP47770

Academic Year 2024/2025

Health Data & Analytics (COMP47770)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Mark Matthews
Trimester:
Summer
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Digital technology has long promised to transform healthcare, yet with little real world impact to date. We are, however, in the midst of a transformation from paper-based and face-to-face healthcare to a world of digitally-enabled, algorithmically-driven treatment.

This module focuses on the intersection between healthcare providers, patients and technology, and provides a practical and theoretical grounding of the key skills needed in this digital transition to a data-driven care model.

Core topics include:
- a brief history of health data and treatment
- understanding and defining health data
- digital biomarkers and digital pheonotyping
- machine-learning for healthcare and related issues
- structured vs. unstructured data
- standards in healthcare information (e.g., HL7 FHIR)
- considerations when designing new data-driven digital health applications

About this Module

Learning Outcomes:

On completion of this module, students will be able to:
1. Understand the current state of the art in digital healthcare
2. Understand key distinctions related to digital health data
3. Identify appropriate system designs to consider when implementing new digital health technologies
4. Understand how to design basic structured health data
5. Be able to identify appropriate data analysis approaches to a range of health challenges

Indicative Module Content:

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

40
Autonomous Student Learning

40
Lectures

24
Total

104

Approaches to Teaching and Learning:
This module will take a hands-on approach to the course content with several real-world case studies and presentations from practitioners in the field grounding learning of the course topics. Core lectures will be supplemented by individual and group presentations that put real world issues related to digital health data and analytics at play.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: Major individual data visualization that students develop from scratch to completion with an accompanying record and codebook. Week 11 Graded Yes
70
 Yes
Exam (In-person): In class exam based on the course content. Week 12 Graded No
20
 No
Portfolio: Github portfolio that includes all lab work and assessments. Week 12 Pass/Fail Grade Scale No
10
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

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