A1 Refereed original research article in a scientific journal
A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury
Authors: Umer A, Mattila J, Liedes H, Koikkalainen J, Lotjonen J, Katila A, Frantzen J, Newcombe V, Tenovuo O, Menon D, van Gils M
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication year: 2019
Journal: IEEE Journal of Biomedical and Health Informatics
Journal name in source: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Journal acronym: IEEE J BIOMED HEALTH
Volume: 23
Issue: 3
First page : 1261
Last page: 1268
Number of pages: 8
ISSN: 2168-2194
eISSN: 2168-2208
DOI: https://doi.org/10.1109/JBHI.2018.2842717
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/41078794
Traumatic brain injury (TBI) occurs when an external force causes functional or structural alterations in the brain. Clinical characteristics of TBI vary greatly from patient to patient, and a large amount of data is gathered during various phases of clinical care in these patients. It is hard for clinicians to efficiently integrate and interpret all of these data and plan interventions in a timely manner. This paper describes the technical architecture and functionality of a web-based decision support system (DSS), which not only provides advanced support for visualizing complex TBI data but also predicts a possible outcome by using a state-of-the-art Disease State Index machine-learning algorithm. The DSS is developed by using a three-layered architecture and by employing modern programming principles, software design patterns, and using robust technologies (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework, etc.). The DSS is comprised of a patient overview module, a disease-state prediction module, and an imaging module. After deploying it on a web-server, the DSS was made available to two hospitals in U.K. and Finland. Afterwards, we conducted a validation study to evaluate its usability in clinical settings. Initial results of the study indicate that especially less experience clinicians may benefit from this type of decision support software tool.
Downloadable publication This is an electronic reprint of the original article. |  |
