Quanticode

Quanticode

qcicon

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.

 

Data Fusion

 

We will deliver a step-change in the ease with which analysts can integrate heterogeneous data.

 

Data governance
Frameworks and guildlines for best trade-off between the use of the data and avoiding ethical issues.

Data linkage
Efficient hierarchical data linkage and capability to handle inexact data mappings and longitudinal changes in hierarchies/mappings.

Visualizing quality
Methods for users to investigate the quality of linked data.

Analytical Techniques

 

We aim to deliver scalable and adaptive methods for processing/mining heterogeneous longitudinal data, and novel visualization interfaces for effective analysis.


Mining longitudinal data
Novel methods to robustly model longitudinal data by considering observational noise and new temporal relations.

Scalability via stochastic control
Using stochastic control techniques to build adaptive models that balance accuracy and computational cost automatically.

Question-posing visual interface
To create low-effort question-posing visual interface that allow users to pose questions in their own terms, and then return time-constraint optimal results.

Abstraction Models

 

Abstraction models are built with ethical and time constraints for visualization and data analytics.

 

Governance-aware abstraction
A tool that implements the governance principles for linkage and data granularity, to create a system that is ethical-by-design to automatically detect suspicious behaviour and reduce the risks of breaching privacy.

Computational and visual techniques for abstraction model building
To build robust abstraction models that condense data, reduce the number of variables, and simplify analysis while preserving important details. High resolution display is expected for the visualization over various granularity.

Abstraction mining
To automatically build models abstractly defined by intuitive examples provided by users.

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).