Health & Medical Projects
The D²iCE medical domain spans both physical and mental health. As medical services are under resourced world-wide and our reliance and expectations on the health system are ever increasing, time and cost savings without concessions to quality of care are of the essence. Machine learning promises to meaningfully contribute to high-quality care with significant resource and cost savings, but to-date this has resulted in limited real-world applications. We collaborate with medical partners to ensure our innovations have the best chance of making a real impact on the lives of real patients. Prof. Pepijn van de Ven leads the Mental & Behavioural Health pillar and Prof. Conor Ryan leads the Physical Health & Medicine pillar.
Ongoing Projects
Mitigating Privacy and Data Security Risks in Healthcare-IoT using AI/ML techniques
In the last few years, the healthcare industry has significantly transformed with the increased use of emerging technologies (i.e., IoT, Cloud, AI/ML, AT/VR, etc.). This raises a major security concern as the IoT devices have not been designed with “a security-in-mind” perspective across the global IoT in the healthcare market.
Fast identification of depressive symptomatology using probabilistic machine learning
Depression is an extremely common disorder, yet often not diagnosed. We aim to use Probabilistic Machine Learning to develop optimised approaches to help the specialists to identify these people, giving them the chance to access proper treatment as soon as possible.
Ultra-brief depression questionnaires
Find the optimal ultra-brief questionnaire for identifying individuals with depressive symptoms.
Personae
Personalised Digital Treatment of Depression
The primary ambition of the project is to develop an intelligent individualization of treatment content and delivery to match the patients' symptoms and life situation.
Completed Projects
Proactive Depression Treatment System
Depression is an extremely common disorder, yet often not diagnosed. We aim to use Probabilistic Machine Learning to develop optimised approaches to help the specialists to identify these people, giving them the chance to access proper treatment as soon as possible.