|Appears in Collections:||Computing Science and Mathematics Book Chapters and Sections|
|Peer Review Status:||Refereed|
|Title:||A novel clinical expert system for chest pain risk assessment|
|Citation:||Farooq K, Hussain A, Atassi H, Leslie S, Eckl C, MacRae C & Slack W (2013) A novel clinical expert system for chest pain risk assessment In: Liu D, Alippi C, Zhao D, Hussain A (ed.) Advances in Brain Inspired Cognitive Systems: 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings, Berlin Heidelberg: Springer. 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, 9.6.2012 - 11.6.2013, Beijing, China, pp. 296-307.|
|Keywords:||RACPC risk assessment|
Chest pain decision support system
Clinical decision support system for chest pain based on NICE Guidelines
|Series/Report no.:||Lecture Notes in Computer Science, 7888|
|Abstract:||Rapid access chest pain clinics (RACPC) enable clinical risk assessment, investigation and arrangement of a treatment plan for chest pain patients without a long waiting list. RACPC Clinicians often experience difficulties in the diagnosis of chest pain due to the inherent complexity of the clinical process and lack of comprehensive automated diagnostic tools. To date, various risk assessment models have been proposed, inspired by the National Institute of Clinical Excellence (NICE) guidelines to provide clinical decision support mechanism in chest pain diagnosis. The aim of this study is to help improve the performance of RACPC, specifically from the clinical decision support perspective. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating 14 risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. In the first phase, a novel binary classification model was developed using a Decision Tree algorithm in conjunction with forward and backward selection wrapping techniques. Secondly, a logistic regression model was trained using all of the given variables combined with forward and backward feature selection techniques to identify the most significant features. The new models have resulted in very good predictive power, demonstrating general performance improvement compared to a state-of-the-art prediction model.|
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