The Health Information Environment and Health Information Systems projects at the Australian e-Health Research Centre aim to improve the health outcomes by building technology which improves the use and effectivness of data captured about the patient.
Data is captured about patients in a number of different formats, electronic repositories and using many different terminologies. Our technologies are targeted at understanding the information in the data, whether the data is captured in an electronic health record, coded in a clinical database, physiological data captured from sensors, described in free text in pathology reports or even captured using imaging technology. Our tools for using and building clinical terminologies can be used to describe the data in a way which is machine readable. The new statistical algorithms and visualisation techniques that form part of our research and development provide analysis and display of the data for clinicians, researchers and patients.
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HDIprovides 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 |
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Clinical Terminology ToolsClinical terminologies are used to collect health information in a standard way. SNOMED CT, now adopted by Australia as the standard clinical terminology, organises its concepts so that computers can reason over the content of electronic health records. The clinical terminologiy tools being developed at the AEHRC include:
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Clinical Data Acquisition, Integration, Visualisation and AnalysisWe are making use of our data acquisition, integration, visualisation and analysis capability to conduct research into neurodegenerative diseases such as Alzheimer's disease and Stroke for the Preventative Health Flagship. More about Clinical Data Management |
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Mining Large Complex DatasetsInformation acquired during clinical treatments can provide valuable knowledge for medical diagnoses, treatments and prognoses. However with such overwhelming amounts of data now being captured, novel techniques are required to process the data and retrieve the information efficiently and effectively. At AEHRC, we work with clinicians across Australia to develop various database and data mining mechanisms for retrieving rich information from large and complex data sets, such as physiological data, ECG and EEG signals. |
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Statistical Modelling and Pattern Recognition of Patient DataThe 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 |
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