UK Partner
Jim Briggs, Director, Centre for Healthcare Modelling and Informatics (CHMI), University of Portsmouth
Ukraine Partner
Name Vira Liubchenko, Professor Department of Software Engineering, Odesa Polytechnic National University
Co-Investigators
Nataliia Komleva, Associated Professor, Department of Software Engineering, Odesa Polytechnic National University
Svitlana Zinovatna, Associated Professor, Department of Software Engineering, Odesa Polytechnic National University
Project objectives
Full-scale military aggression on the territory of Ukraine poses a significant threat to the health of Ukrainians. During and after the war, one must be prepared for mass manifestations of diseases resulting from prolonged stress, such as PTSD. It can be expected that many people will not pay attention to the symptoms and will not go to the doctor.
Therefore, a methodology for the automated detection of health problems based on the analysis of several measurable indicators is applicable. Data analysis techniques can become the basis for identifying an alarming situation and generating a signal for medical intervention.
This approach will increase the efficiency of detecting "latent" diseases, which is especially important in conditions of high workload on doctors and the affected medical infrastructure.
Project goals
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.
Role of each Partner
UK-team collaborates in benchmarking analysis and consults UA-team during the development phase.
UA-team is responsible for the methodology development and the software prototype implementation.
Timing
Project Start Date: 1 February 2023
Project End Date: 31 August 2023
Expected results
1. Publication of a joint research article
2. Dissemination of the joint research works at a relevant conference
