COMP53315: Innovative Technologies for Health

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• 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.

• 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.

Subject-specific Knowledge:

• 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.

Subject-specific Skills:

• 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.

Key Skills:

• 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.

• 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.

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