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Intensive Care patients can experience Adverse Events associated with sudden episodes of low blood pressure. These Adverse Events may impact all of the main organs resulting in longer lengths of stay, increased care costs and reducing quality of outcomes. Existing technologies enable clinicians to know when these events have occurred and treat the effects.

Medical techniques for avoiding Adverse Events currently exist, but clinicians don’t have a reliable way to predict the occurrence, so there’s no opportunity for intervention.

Research indicates average lengths of stay can be reduced by up to 30%, and outcomes improved for a similar proportion of patients, if these Adverse Events can be avoided through prediction and intervention. Potential savings across the EU exceed 5 billion euros, annually.

A model for predicting Adverse Events offers potential for improving outcomes across a wide range of conditions and or illnesses.

The Avert-IT project envisages the development of a novel bed-side monitoring and alerting system dedicated to the prediction and notification to physicians and nursing staff of variations in the condition of the patient that are likely to lead to hypotension without appropriate clinical intervention.


The main scientific objective of the project is the determination of the weighted association
between multiple patient parameters and subsequent arterial hypotension. The association will then be used to define the novel Bayesian neural network, which will be trained against the BrainIT dataset, before undertaking a clinical trial to demonstrate the Avert-IT project concept.

The main technological objective will be the development of an IT-based decision support system (“HypoPredict”) appropriate for deployment within intensive and high dependency care units. The system will be capable of:

  • Automatically and continually monitoring at least four in-vivo patient parameters (eg: ECG, arterial blood pressure, Oxygen saturation and core temperature), together with open interfaces providing input of key demographic data (age, gender etc.) and periodic data (clinical pathology results etc.) related to the patient.
  • Outputting a continuous Hypotension Prediction Index (HPi) which will be updated on a minute by minute basis upon any change detected in the patient parameter input set.


The project will also look to develop an exploitation model for the commercialisation of the software in product/service sales across international markets. For such commercial exploitation, C3 Global will have exclusive access to the results of the research. Potential opportunities include:

  • Monitoring of intensive care patient treatment
  • Clinical trials of both drugs and medical devices
  • BANN (Bayesian Artificial Neural Network) techniques for asset performance management and environmental monitoring and control

Expected Results & Impacts

  • Accurate prediction of hypotension, allowing earlier and less aggressive intervention, would dramatically improve patient outcome, leading in turn to a quantifiable reduction in the duration of average patient stay with associated reduction in cost.
  • Capability to alert clinicians of upcoming hypotensive episodes
  • Core technology for applying Bayesian Artificial Neural Network concepts in health care applications.
  • Platform for multi centre collaborative research
  • Facilities supporting clinical governance and evidence based medicine