Australian e-Health Research Centre
Australian e-Health Research Centre Australian e-Health Research Centre

Health Information Environment

Australian e-Health Research Centre researchers and engineers are developing novel applications for the processing and use of health and medical data. Health and medical data is stored using any number of technologies and formats with differing requirements of privacy and security.

The technical and application domains which are of interest to our researchers and engineers include:

  • database and data integration technology
  • clustering and analysis of patient data
  • electronic health records
  • ontology engineering
  • query and retrieval of complex data
  • natural language processing

Health Data Integration (HDI)

HDI™ – which will provide private and secure access to an integrated virtual data repository, enabling research and analysis on a larger scale than would be possible on the individual data repositories alone.

More about HDI

SNOMED CT for integrating and querying health data

The SNOMED CT clinical terminology is one of the largest examples of an ontology and has been endorsed by Australian health agencies and the National E-Health Transition Authority (NEHTA) as the clinical terminology to be used in the collection, management and sharing of health and health related data.

The Australian e-Health Research Centre is building tools which to describe data which already exists in databases to be described using SNOMED CT, and building applications which use those mapping to demonstrate the value of capturing the data in SNOMED CT.

More about using large ontologies

snorocket: Ontology Classification Engine

snorocket™ is an implementation of the Dresden algorithm that is tuned for classifying the SNOMED CT clinical terminology. As the name suggests, snorocket is fast, able to classify SNOMED CT at least an order of magnitude faster than other known classifiers. snorocket™ will underpin the development of other solutions at AEHRC which use the SNOMED CT terminology for integrating, querying or retrieving health and health related data. snorocket™ provides a simple API for supporting third party tools with the need for fast classification of large ontologies.

More about snorocket™

Querying of Large Complex Datasets

Information captured during clinical treatment can provide valuable knowledge for future treatment, both for the patient involved and for other patients. With such a flood of data now being captured, novel mechanisms are required to process the data and retrieve the information.

Making best use of the large amount of monitoring data being captured, such as EEG and ECG, not only requires new ways of processing the data but new and inventive ways of using the data.

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Statistical modelling and pattern recognition of patient data

The use of ontologies to understand the relationships between data and new mechanisms of processing and integrating data will potentially lead to large datasets which can be analysed to provide information about health service delivery, the initiation and progression -- (or natural history) -- of particular diseases.

The health data and information group at the Australian e-Health Research centre is working with clinicians in Queensland, and across Australia through the CSIRO Preventative Health Flagship, to use advanced statistical methods to capture knowledge from clinical data.

More about statistical modelling and pattern recognition of patient data