Medical Imaging and Biomedical Informatics

As an informatics area it is primarily concerned with data plus meaning (also known as semantics). It provides methods to tackle concepts that are hard to capture using formal, computational definitions.

While it is used synonymously with health informatics by many, we distinguish our group’s interests by being more focused on hard aspects.

These include:

• Formal information and knowledge methods and standards
• Semantic web
• Digital patient
• Decision support
• Interoperability
• Intelligent image analysis
• Structural and functional brain connectivity
• AI guided diagnosis and surgical planning
• Biomedical modeling and simulation
• Software and development work.

The aim of our group is to link clinical information, image information and workflows to computational models, methods and tools, in order to bring about personalised, predictive and quantitative approaches to Biomedicine, and help realise the Physiome Project/Virtual Physiological Human initiatives. This, we believe, will pave the way to new breakthroughs and coming of next generation clinical decision support tools and new technologies.

MedTech CoRE – Platform 5: Software and Modelling

The aim of the project is to represent measurement data and associated clinical information using openEHR archetypes and then map to CellML and FieldML parameters for model validation, and to create next generation of personalised and predictive decision support tools at the bedside.

An openEHR compliant research data repository will be set up to store clinical and experimental which will allow for semantic annotation using biomedical ontologies (for example FMA, Human Phenotype Ontology etc.) and clinical terminology (for example SNOMED CT, LOINC etc.).

Innovative computational methods for Imaging Informatics  

We are devoted to developing AI-guided methods and tools in medical image processing, medical information mining, brain sciences, specialty customized surgical planning and therapy, and disease detection & prediction.

Annotation of clinical datasets using openEHR Archetypes

Linking of computational models and clinical data requires agreement on common semantics, which should be understood by computers without human intervention. This project aims to use the openEHR electronic health record (EHR) standard as the semantic glue to make this connection.

Health information models, called Archetypes, will be annotated using shared ontologies. They will be utilised by the Physiome Model Repository (PMR) as meta-data, to link to associated computational models. The aim is to create an advanced semantic search functionality that can drive the development of model and data-driven clinical decision tools and new knowledge discovery.

For more information: Annotation of clinical datasets using openEHR Archetypes as a solution for data access issues faced in biomedical projects.

Primary contact

David Nickerson

Academics

David Nickerson
Alan Wang