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Peer Review Status: Refereed
Title: An ontology driven and Bayesian Network based cardiovascular decision support framework
Author(s): Farooq, Kamran
Hussain, Amir
Leslie, Stephen
Eckl, Chris
MacRae, Calum
Slack, Warner
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Editor(s): Zhang, H
Hussain, A
Liu, D
Wang, Z
Citation: Farooq K, Hussain A, Leslie S, Eckl C, MacRae C & Slack W (2012) An ontology driven and Bayesian Network based cardiovascular decision support framework. In: Zhang H, Hussain A, Liu D & Wang Z (eds.) Advances in Brain Inspired Cognitive Systems: 5th International Conference, BICS 2012, Shenyang, China, July 11-14, 2012. Proceedings. Lecture Notes in Computer Science, 7366. Berlin Heidelberg: Springer, pp. 31-41.
Keywords: cardiovascular decision support system
ontology driven decision support with uncertainty modeling
clinical decision support and Bayesian Network
Issue Date: 2012
Date Deposited: 8-Aug-2013
Series/Report no.: Lecture Notes in Computer Science, 7366
Abstract: Clinical risk assessment of chronic illnesses in the cardiovascular domain is quite a challenging and complex task which entails the utilization of standardized clinical practice guidelines and documentation procedures to ensure clinical governance, efficient and consistent care for patients. In this paper, we present a cardiovascular decision support framework based on key ontology engineering principles and a Bayesian Network. The primary objective of this demarcation is to separate domain knowledge (clinical expert's knowledge and clinical practice guidelines) from probabilistic information. Using ontologies is a cost effective and pragmatic solution to implementing a shift from simple patient interviewing systems to more intelligent systems in primary and secondary care. The key components of the proposed cardiovascular decision support framework have been developed using an ontology driven approach. We have also utilized a Bayesian Network (BN) approach for modelling clinical uncertainty in the Electronic Healthcare Records (EHRs). The cardiovascular decision support framework has been validated using a sample of real patients' data acquired from the Raigmore Hospital's RACPC (Rapid Access Chest Pain Clinic). A variable elimination algorithm has been used to implement the BN Inference and clinical validation of the "Coronary Angiography" treatment has been carried out using Electronic Healthcare Records.
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