Medical Knowledge and Decision Support

We advance “Medical Knowledge and Decision Support” with data science and artificial intelligence, and qualitatively explore digital transformation in the clinic.

The overarching objectives of the research of the Medical Knowledge and Decision Support group are to implement innovative data science approaches, accelerate discovery, translation and integration of evidence into clinics, and to align technological progress with the needs of clinicians.

Methods

Our research draws methodologically from data science, bioinformatics, computer science, and psychology. We build ontologies and an associated ecosystem for logic-based automation. We apply and develop predictive models using AI/ML, particularly LLMs and neuro-symbolic approaches. We apply mathematical approaches including constraint-based modelling. In addition, we use qualitative approaches to study human perspectives.

Use Cases

We address a wide range of application use cases across our projects, including the discovery and prediction of functions and regulatory factors, biomedical and clinical evidence synthesis and automation, pre-trained large language and multi-modal model evaluation, extraction of data from clinical notes for predictive modelling, decision support, medical question answering, and interpretability and explanations for AI applications.

Clinical Areas

Our projects span a range of biomedical and clinical domains, including fundamental metabolism and metabolomics; drug discovery and molecular property prediction; RNA biology and regulatory interactions; oncology and personalised cancer biology; behavioural science and health psychology; mental health including anxiety, depression and psychosis; ophthalmology; infectious diseases; and physiotherapy.

Learn more about our projects