TY - JOUR
T1 - A decision support system for diagnostics and treatment planning in traumatic brain injury
AU - Umer, Adil
AU - Mattila, Jussi
AU - Liedes, Hilkka
AU - Koikkalainen, Juha
AU - Lotjonen, Jyrki
AU - Katila, Ari
AU - Frantzen, Janek
AU - Newcombe, Virginia
AU - Tenovuo, Olli
AU - Menon, David
AU - Van Gils, Mark
N1 - Funding Information:
This work was supported in part by the 7th Framework Program by the European Commission in the cofunded Project TBIcare (GA-270259). The work of D. Menon was supported by the National Institute for Health Research (NIHR), U.K. through the NIHR Cambridge Biomedical Centre and a Senior Investigator Award.
Funding Information:
Manuscript received November 8, 2017; revised March 28, 2018 and May 18, 2018; accepted May 28, 2018. Date of publication June 1, 2018; date of current version May 6, 2019. This work was supported in part by the 7th Framework Program by the European Commission in the cofunded Project TBIcare (GA-270259). The work of D. Menon was supported by the National Institute for Health Research (NIHR), U.K. through the NIHR Cambridge Biomedical Centre and a Senior Investigator Award. (Corresponding author: Adil Umer.) A. Umer, H. Liedes, and M. van Gils are with the VTT Technical Research Centre of Finland Ltd., Tampere 1000, Finland (e-mail:, [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - Clinical decision support
KW - traumatic brain injury
KW - web-based tool
UR - https://www.scopus.com/pages/publications/85047979138
U2 - 10.1109/JBHI.2018.2842717
DO - 10.1109/JBHI.2018.2842717
M3 - Article
C2 - 29993563
AN - SCOPUS:85047979138
SN - 2168-2194
VL - 23
SP - 1261
EP - 1268
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 3
M1 - 8370619
ER -