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Health informatics and health data science

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Health informatics
  1. Providing interoperability between different clinical systems, across care settings’ boundaries, and semantically integrating clinical and research systems (e.g., with digitally enhanced medical terminology services and ontology-based approaches) in order to achieve effective impact of research into practice.
  2. Bridging the translational gap between research and care provision, through the innovative development of e-trial systems, novel analytics methodologies and meta-analysis approaches.
  3. Implementing new integrated care platforms to measure, analyse and communicate health data to all levels of healthcare delivery.
  4. Promoting wellbeing through patient-empowerment solutions (e.g., m-health and wellbeing apps) and supporting patient behavioural change.
  5. Modelling and analysing quality and service improvements of care, through healthcare systems engineering approaches.
  6. Clinical IT Safety, including areas on digital architectures improving patient safety, safety analysis techniques (e.g. FMEA) for healthcare services, assurance of complex socio-technical healthcare services, interpretation of safety standards such as SCCI 0160, and ISO 13485, clinical safety cases, and failure analysis of integrated digital healthcare.


Health datascience
  1. Making sense of health and wellbeing data, in order to improve public health and clinical knowledge in the characterisation and management of disease, through novel biomedical informatics technologies (e.g., connected healthcare information systems, electronic healthcare records, clinical decision support systems, etc.).
  2. Developing methodologies, based on advanced statistical learning, machine learning and AI approaches, for the analysis of complex biomedical datasets in the context of non-communicable and chronic diseases in order to achieve a personalized medicine perspective in the provision of care.
  3. Developing further the emerging field of Radiomics to achieve more accurate prediction of disease progression, responsivity to treatments, and patients’ survival in cancer. In particular, investigating the application of informatics and advanced computer algorithms to provide an intelligent decision support aid to clinicians for the characterisation of childhood brain tumours.


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