Quanticode

Quanticode

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Intelligent Infrastructure for Quantitative, Coded Longitudinal Data

The project has been funded under the EPSRC Making Sense of Data call, which is part of the Towards an Intelligent Information Infrastructure (TI3) cross-ICT priority. The quantiCode project aims to develop novel data mining and visualization tools and techniques, which will transform people’s ability to analyse quantitative and coded longitudinal data. Such data are common in many sectors. For example, health data is classified using a hierarchy of hundreds of thousands of Read Codes (a thesaurus of clinical terms), with analysts needing to provide business intelligence for clinical commissioning decisions, and researchers tacking challenges such modelling disease risk stratification. Retailers such as Sainsbury’s sell 50,000+ types of products, and want to combine data from purchasing, demographic and other sources to understand behavioural phenomena such as the convenience culture, to guide investment and reduce waste.

We aim to deliver an infrastructure that provides far more powerful analytical tools than those are available today for public and private sector organizations to transform their abilities to analyze quantitative and coded longitudinal data.

Our goals include:

  • To understand the workflows and to address the barriers of knowledge extraction from data in private and public sectors.
  • Thought leadership in data governance.
  • Efficient heterogeneous data fusion.
  • Robust and scalable data mining/machine learning tools for data analysis.
  • Data visualization/mining of abstraction models.

 

Our Team

Our Partners

Our partners come from the public and private sectors:

  • AQL
  • Consumer Data
  • J. Sainsbury’s
  • Leeds North Clinical Commissioning Group
  • Bradford Institute for Health Research
  • Health & Social Care Information Centre (HSCIC)
  • Leeds City Council

Contact Us

For any enquiries, please contact: Prof. Roy Ruddle (PI),r.a.ruddle@leeds.

Acknowledgements

The QuantiCode project is funded by the EPSRC (EP/N013980/1) and supported by the MRC (ES/L011891/1) and the ESRC (ES/L011891/1).