Module COMP53315: Innovative Technologies for Health

Department: Computer Science

COMP53315: Innovative Technologies for Health

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To enable students to design and implement computational solutions that analyse healthcare data, build predictive models, and generate actionable recommendations to improve patient outcomes.
• To position students to critically engage with the ethical and practical challenges of deploying computational tools in healthcare and to extend their expertise through future study and interdisciplinary collaboration.

Content

• Ethics and privacy: principles of fairness, privacy, bias mitigation, and compliance with regulations like GDPR.
• Predictive models for healthcare: building models for disease diagnosis, treatment recommendations, and patient monitoring
• Healthcare applications: sequential modelling for time-series data, natural language processing for clinical notes, or medical imaging analysis.
• Hands-on tools: data preprocessing, feature engineering, and working with datasets
• Model Evaluation and Recommendations: Healthcare-specific metrics (sensitivity, specificity) and generating actionable recommendations.
• Clinical Decision Support Systems (e.g. predictive/risk modelling, text analysis, and image processing)
• Emerging Trends: covering telemedicine, wearable devices, and AI innovations for personalised care, health/mental health and evaluations of the effectiveness of digital interventions.

Learning Outcomes

• By the end of this module, students should have:
• an understanding of predictive models for healthcare applications.
• an in-depth knowledge of healthcare applications.
• an understanding of model evaluation and recommendations.

• By the end of this module, students should be able to demonstrate:
• an ability to apply computational methods to design predictive models and decision-support systems for healthcare challenges.
• an ability to evaluate the ethical, societal, and privacy implications of deploying computational tools in healthcare.
• an ability to design and implement computational tools that improve patient care and outcomes.
• an ability to critically analyse real-world healthcare datasets to extract actionable in-sights and recommendations.

• By the end of this module, students should:
• be equipped with the skills to build and validate predictive models for healthcare applications, such as disease diagnosis, patient monitoring and treatment recommendations.
• have the ability to translate computational models into real-world applications that improve patient care and outcomes.
• be prepared for future interdisciplinary collaboration by integrating perspectives from data science, clinical healthcare and ethics.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures enable the students to learn new material relevant to CS for Health, as well as their applications.
• Computer classes enable students to acquire necessary coding skills, learn about the relevant libraries and packages and receive feedback on their work.
• The summative assessment assesses the learnt knowledge and application of methods and techniques.
• The assignment element of the coursework component consists of a coding exercise with accompanying report.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Via computer classes.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University