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

Area of Science:

• Neurosurgery
• Medical Informatics
• Machine Learning

Background:

• Traumatic brain injury (TBI) presents complex clinical data, challenging timely interpretation and intervention planning for healthcare providers.
• Effective management of TBI requires integrating diverse patient information gathered throughout clinical care phases.
• Current clinical workflows struggle with efficiently processing and utilizing the extensive data associated with TBI.

Purpose of the Study:

• To describe the technical architecture and functionality of a novel web-based decision support system (DSS) for traumatic brain injury (TBI) management.
• To introduce a DSS that integrates advanced data visualization with machine learning-based outcome prediction for TBI patients.
• To present a system designed to assist clinicians in interpreting complex TBI data and planning interventions.

Main Methods:

• Development of a web-based DSS utilizing a three-layered architecture and modern programming principles (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework).
• Implementation of a Disease State Index machine-learning algorithm for predicting patient outcomes.
• Inclusion of modules for patient overview, disease-state prediction, and medical imaging analysis.

Main Results:

• The DSS was deployed on a web server and validated in clinical settings across two hospitals in the UK and Finland.
• The system successfully integrates data visualization and predictive analytics for traumatic brain injury (TBI).
• Initial usability study results suggest significant benefits, particularly for less experienced clinicians.

Conclusions:

• The developed web-based decision support system offers advanced capabilities for managing complex traumatic brain injury (TBI) data.
• The DSS, incorporating machine learning for outcome prediction, shows promise in enhancing clinical decision-making for TBI.
• The system's usability indicates its potential to support clinicians, especially those with less experience, in TBI patient care.