A decision support system for diagnostics and treatment planning in traumatic brain injury

Adil Umer; Jussi Mattila; Hilkka Liedes; Juha Koikkalainen; Jyrki Lotjonen; Ari Katila; Janek Frantzen; Virginia Newcombe; Olli Tenovuo; David Menon; Mark Van Gils

doi:10.1109/JBHI.2018.2842717

A decision support system for diagnostics and treatment planning in traumatic brain injury

Adil Umer, Jussi Mattila, Hilkka Liedes, Juha Koikkalainen, Jyrki Lotjonen, Ari Katila, Janek Frantzen, Virginia Newcombe, Olli Tenovuo, David Menon, Mark Van Gils

Research output: Contribution to journal › Article › Scientific › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Article number	8370619
Pages (from-to)	1261-1268
Number of pages	8
Journal	IEEE Journal of Biomedical and Health Informatics
Volume	23
Issue number	3
DOIs	https://doi.org/10.1109/JBHI.2018.2842717
Publication status	Published - May 2019
Externally published	Yes
Publication type	Not Eligible

Funding

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. 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]).

Keywords

Clinical decision support
traumatic brain injury
web-based tool

ASJC Scopus subject areas

Biotechnology
Computer Science Applications
Electrical and Electronic Engineering
Health Information Management

Access to Document

10.1109/JBHI.2018.2842717

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Umer, A., Mattila, J., Liedes, H., Koikkalainen, J., Lotjonen, J., Katila, A., Frantzen, J., Newcombe, V., Tenovuo, O., Menon, D., & Van Gils, M. (2019). A decision support system for diagnostics and treatment planning in traumatic brain injury. IEEE Journal of Biomedical and Health Informatics, 23(3), 1261-1268. Article 8370619. https://doi.org/10.1109/JBHI.2018.2842717

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abstract = "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.",

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note = "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: {\textcopyright} 2018 IEEE.",

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A decision support system for diagnostics and treatment planning in traumatic brain injury

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