The Platform for Enhanced Analytics and Computational Healthcare (PEACH) is a project being delivered by the Department of Computer Science at University College London (UCL) and University College London Hospitals (UCLH). The project has two primary objectives, to build a data anonymisation tool as well as extending the PEACH’s core analytics system by implementing a visualisation solution.
In this project, we have created a data anonymisation tool designed for healthcare which combines a simple user interface for medical professionals and advanced privacy models to anonymise sensitive patient data. The anonymisation solution is built for use in various areas, including for research and for statistical records.
The core analytics system provides a modern data analytics suite designed to support routine care, business intelligence and research. During this project, we have created a data visualisation tool and integrated it into the analytics system so analytics data can be visualised and easily accessible to users. Medical researchers are be able to create interactive graphs, charts and displays to gain effective insight into medical data.
With an open and innovative platform, this project allows medical professionals and researchers to use data to make advances in medicine through research. This is whilst protecting the sensitive information the NHS holds on its patients.
Anonymises sensitive medical data by removing or randomising any identifying information.
Generates novel datasets using random entries in provided data.
Supports different file formats including csv, xml and json.
Provides users with anonymisation and data generation options.
Has been developed using Java, JavaFX, Spring and the ARX library.
Visualises data produced by the core analytics system.
Provides users with interactive and customisable displays and graphics.
Easily scalable to handle huge amounts of data.
Has been deployed with Kibana, Elastic Stack and Docker.
Analytics system built using Apache Kafka, Apache NiFi, Apache ZooKeeper, Spark and Microsoft Azure.