It’s a health data mining project to develop a predictive model to better healthcare management
Deakin University has signed a partnership with Max Healthcare for a project that will focus on data analytics for healthcare. Both partners are bringing ‘big data’ to work for medicine with its huge records of patient history – with data on admissions, diagnosis and outcomes, spanning a huge inventory of images, computerised records and registries and the consequent untapped potential to identify critical safety issues, as well as service and clinical efficiencies.
As part of the agreement, Max Healthcare and Deakin University will jointly address this pressing need by leveraging state-of-art and verified techniques in data analytics to support clinical decisions. The outcomes are critically important from economic, patient safety and systems perspectives. The immediate project will focus on heart
disease, specifically on patients with index admission [symptoms] of acute myocardial infarction (AMI) or stroke.
Prof Svetha Venkatesh, Director, Pattern Recognition and Data Analytics, Deakin University said, “The primary objective of the project will be to search through the existing data sets for hidden patterns of both the predictable and preventable events in managing the healthcare of individuals. This will be done by building sophisticated predictive models, utilising machine learning techniques derived from anonymised hospital patient records from diverse hospital data sources. This paradigm is novel, since it is capable of both hypothesis generation and testing, whilst being agnostic and unbiased to prior assumptions.”
A joint team will analyse the data, extract relevant features and build a validated machine learning model for specific prediction task. The resultant model will be jointly held and shared by the teams at Deakin and Max healthcare as a prototype programme. Once successful, the model would be subsequently piloted at Max Healthcare on prospective cases over a period of one year and the predictive accuracy would be calculated and shared with Deakin team.
EH News Bureau
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