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

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, and 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 UK 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.

Toward an Interdisciplinary Approach to Constructing Care Delivery Pathways From Electronic Health Care Databases to Support Integrated Care in Chronic Conditions: Systematic Review of Quantification and Visualization Methods

“…Finally, 14 studies were included from 14 reports (Figure 2 [13]). Of the 14 studies, 10 (71%) were performed in English-speaking countries: the United States [6,[18][19][20][21], United Kingdom [22][23][24], Australia [25], and New Zealand [26]; the rest (n=4, 29%) were performed in Italy [27], China [28], Finland [29], and Germany [30]. A total of 11 (79%) articles were published after 2011 [6,[18][19][20][21][22][23][24][28][29][30], and 3 (21%) articles were published by the same group [6,18,21].…”

“…Of the 14 studies, 10 (71%) were performed in English-speaking countries: the United States [6,[18][19][20][21], United Kingdom [22][23][24], Australia [25], and New Zealand [26]; the rest (n=4, 29%) were performed in Italy [27], China [28], Finland [29], and Germany [30]. A total of 11 (79%) articles were published after 2011 [6,[18][19][20][21][22][23][24][28][29][30], and 3 (21%) articles were published by the same group [6,18,21]. We identified 1 (7%) protocol [24], 12 (86%) descriptive studies [6,[18][19][20][21][22][23][24][25][26][27][28][29], and 1 (7%) validation study [30].…”

“…A total of 11 (79%) articles were published after 2011 [6,[18][19][20][21][22][23][24][28][29][30], and 3 (21%) articles were published by the same group [6,18,21]. We identified 1 (7%) protocol [24], 12 (86%) descriptive studies [6,[18][19][20][21][22][23][24][25][26][27][28][29], and 1 (7%) validation study [30]. All but 1 (7%) study had descriptive objectives, that is, presented the method and its development.…”

Diagnosis Support System for General Diseases by Implementing a Novel Machine Learning Based Classifier

“…The Hoeffding Tree algorithm has been produced by Domingos and Hulten, an exponential tree-level methodology to learn from consecutive information streams [9]. Researchers [15] has implemented new profound recurrent network architecture for automatic identification of patients with particular scalp EEG seizures. He suggested a mapping which would concurrently enable the suggested medico clinical characteristics of EEG cranes.…”

“…This knowledge is used to forecast possible outcomes for each and every person in various mathematical patterns depending on empirical technology. There are Disease Diagnosis Support Systems for several illnesses which analyze previous information from comparable patients and offers personalized therapy suggestions [15]. The support technologies for disease diagnosis have demonstrated improved methods of prescription, decrease major mistakes in medicines, enhance the provision of preventive services and enhance compliance with suggested standards in treatment.…”