Purpose: This multidisciplinary industrial research project sets out to develop a hybrid clinical decision support mechanism (inspired by ontology and machine learning driven techniques) by combining evidence, extrapolated through legacy patient data to facilitate cardiovascular preventative care. Methods:The proposed cardiovascular clinical decision support framework comprises of two novel key components:(1) Ontology driven clinical risk assessment and recommendation system (ODCRARS) (2) Machine learning driven prognostic system (MLDPS). State of the art machine learning and feature selection methods are utilised for the prognostic modelling purposes. The ODCRARS is a knowledge-based system which is based on clinical expert's knowledge, encoded in the form of clinical rules engine to carry out cardiac risk assessment for various cardiovascular diseases. The MLDPS is a non knowledge-based/data driven system which is developed using state of the art machine learning and feature selection techniques applied on real patient datasets. Clinical case studies in the RACPC, heart disease and breast cancer domains are considered for the development and clinical validation purposes. For the purpose of this paper, clinical case study in the RACPC/chest pain domain will be discussed in detail from the development and validation perspective. Results:The proposed clinical decision support framework is validated through clinical case studies in the cardiovascular domain. This paper demonstrates an effective cardiovascular decision support mechanism for handling inaccuracies in the clinical risk assessment of chest pain patients and help clinicians effectively distinguish acute angina/cardiac chest pain patients from those with other causes of chest pain. Conclusion:The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. Various chest pain risk assessment prototypes have been developed and deployed online for further clinical trials.

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We discuss work-in-progress and propose an ontology driven framework for the development of a clinical expert system for chest pain risk assessment. The framework has the following key components: adaptive questionnaire, patient medical history, risk assessment and decision support. We intend to incorporate a range of chest pain assessment guidelines and risk scoring rules within the system and design the framework so it can be extended to a range of other cardiovascular diseases in various clinical settings.

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.