Methodology for illness detection by data analysis techniques

The research aims to develop a methodology for early detection of the signals pointing to a developing medical emergency that draws on data analytics. To achieve the aim, the following objectives are planned:

− benchmarking analysis of ViEWS methodology to determine the time of data actuality, accuracy level for model usefulness, the requirements on the frequency of data recording, and so on;
- working with a given dataset to develop techniques for the determination of the minimum and sufficient indicators set among informative features, taking into account the individual characteristics of the patient when interpreting the data, the requirements to data gathering conditions;
- experiments with transferring the methodology to another dataset representing another set of vital signs data.

A developed software prototype for data modelling and visualization for the experiments will demonstrate the methodology and its potential implementation.
The research results by CHMI, especially the predictive model ViEWS (VitalPAC Early Warning Score), will underpin the proposed research.