Abstract
Abstract 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.
Highlights
The adoption of clinical decision support systems (CDSSs) in the diagnosis and administration of major chronic diseases e.g. (Dementia Lindgren 2011), cancer, diabetes (OConnor et al 2011), hypertension (Luitjes et al 2010) and heart disease (DeBusk et al 2010) have made significant contributions in improving the clinical outcomes at primary and secondary care healthcare organisations all over the world
The rest of this paper will be in sections: In “Background” section, we provide a detailed description of the novel machine learning driven prognostic system based on the chest pain clinical case study and the complete development life cycle followed by validation results
It has been brought to light as a result of this clinical case study that we do not need all of the expensive lab tests to figure out if patient is presenting cardiac chest pain related symptoms
Summary
MLDPS development based on rapid access chest pain clinic’s clinical case study An iterative development process, based on machine learning and feature selection has been utilised in the development of machine learning driven prognostic models. Two new study cohorts were created for this purpose as shown, so that a comparison could be drawn among two study groups Another clinical requirement was to compare the clinical effectiveness of two models separately and to classify chest pain patients Candidate clinical variables preselected by the clinical domain expert were classified using the LR, DT and SVM classifiers and results were compared with the state of the art feature selection methods as shown in our experimental setups. The purpose of expert-driven (ED) data classification was to develop a baseline model using the LR classifier As it can be seen, the LR based classification setups combined with backward feature selection method (smoker, number of years smoking, age, diabetes type and raised cholesterol) were able to classify the RACPC patient dataset with a classification accuracy of 68.99 %. In the case of SVM (linear kernel function), similar clinical variables were picked up by the BS wrapping technique
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