A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 3; pp. 1261 - 1268
• Main Authors: Umer, Adil, Mattila, Jussi, Liedes, Hilkka, Koikkalainen, Juha, Lotjonen, Jyrki, Katila, Ari, Frantzen, Janek, Newcombe, Virginia, Tenovuo, Olli, Menon, David, van Gils, Mark
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain Injuries, Traumatic - diagnosis; Brain Injuries, Traumatic - therapy; Clinical decision support; Computer architecture; Computer programs; Data models; Data visualization; Decision support systems; Decision Support Systems, Clinical; Diseases; Head injuries; Hospitals; Humans; HyperText Markup Language; Imaging; Internet; Learning algorithms; Machine learning; Modules; Neuroimaging; Patients; Software; Software development tools; Structure-function relationships; Traumatic brain injury; web-based tool; Webs (structural)
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2018.2842717

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.