An Ontology Driven and Bayesian Network Based Cardiovascular Decision Support Framework

Farooq, Kamran; Hussain, Amir; Leslie, Stephen J; Eckl, Chris; MacRae, Calum A.; Slack, Warner V.

doi:10.1007/978-3-642-31561-9_4

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…This article presents the use of a BN approach for discovering the relationships between demographic and musculoskeletal symptom variables, and the development PsA in people with psoriasis. BNs have been used for various applications in a wide variety of domains, including, but not limited to, medical diagnosis, disease prediction, clinical decision-making, and risk prediction [ 22–27 ]. BNs not only provide a robust and flexible approach, but they are also able to handle uncertainty and integrate clinical knowledge alongside data-driven methods to infer structure from data.…”

Section: Discussionmentioning

confidence: 99%

Using Bayesian networks to identify musculoskeletal symptoms influencing the risk of developing psoriatic arthritis in people with psoriasis

et al. 2021

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Objectives To explore Bayesian networks (BNs) in understanding the relationships between musculoskeletal symptoms and the development of PsA in people with psoriasis. Methods Incident cases of psoriasis were identified between 1998 and 2015 from the UK Clinical Research Practice Datalink. Musculoskeletal symptoms identified by medcodes were concatenated into primary groups, each made up of several sub-groups. Baseline demographics for gender, age, body mass index (BMI), psoriasis severity, alcohol use and smoking status were also extracted. Several BN structures were composed using a combination of expert knowledge and data-oriented modelling using: 1) primary musculoskeletal symptom groups, 2) musculoskeletal symptom sub-groups and 3) demographic variables. Predictive ability of the networks using the receiver operating characteristic curve (AUC) was calculated. Results Over one million musculoskeletal symptoms were extracted for the 90,189 incident cases of psoriasis identified, of which 1409 developed PsA. The BN analysis yielded direct relationships between gender, BMI, arthralgia, finger pain, fatigue, hand pain, hip pain, knee pain, swelling, back pain, myalgia and PsA. The best BN, achieved by using the more site-specific musculoskeletal symptom sub-groups, was 76% accurate in predicting the development of PsA in a test set and had an AUC of 0.73 (95% confidence interval (CI): 0.70-0.75). Conclusion The presented BN model may be a useful method to identify clusters of symptoms that predict the development of PsA with reasonable accuracy. Using a BN approach, we have shown that there are several symptoms which are predecessors of PsA, including fatigue, specific types of pain, and swelling.

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Section: Discussionmentioning

confidence: 99%

Using Bayesian networks to identify musculoskeletal symptoms influencing the risk of developing psoriatic arthritis in people with psoriasis

et al. 2021

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“…Some of these systems were based on building their knowledge base using ontology. In diagnosing heart diseases, [22] proposed a Decision Support System to be used by the cardiovascular experts and the data obtained from the Rapid Access Chest Pain Clinic in England. The medical conditions were modelled as binary clauses, either yes or no.…”

Section: Related Workmentioning

confidence: 99%

An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

Al-Hamadani¹

2016

ijacsa

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Abstract-In all the regions of the world, heart failure is common and on raise caused by several aetiologies. Although the development of the treatment is fast, there are still lots of cases that lose their lives in emergence sections because of slow response to treat these cases. In this paper we propose an expert system that can help the practitioners in the emergency rooms to fast diagnose the disease and advise them with the appropriate operations that should be taken to save the patient's life. Based on the mostly binary information given to the system, Bayesian Network model was selected to support the process of reasoning under uncertain or missing information. The domain concepts and the relations between them were building by using ontology supported by the Semantic Web Rule Language to code the rules. The system was tested on 105 patients and several classification functions were tested and showed remarkable results in the accuracy and sensitivity of the system.

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“…An AI inspired ontology driven and machine learning clinical decision support framework as shown in Fig 1 was proposed in [9] to automate clinical risk assessment of RACPC patients which was further developed to include uncertainty modelling in clinical practice guidelines using Bayesian Networks which is explained in detail in [10],followed by implementation of semantically inspired electronic healthcare records in [11]. As part of this EPSRC Industrial Case study a retrospective clinical case study was conducted under the supervision of consultant cardiologist, Professor Stephen Leslie from Raigmore Hospital in Inverness, UK and a data of 632 chest pain patients were collated to validate the proposed decision support framework.…”

Section: A Novel Cardiovascular Decision Support Framework For Efmentioning

confidence: 99%

A novel cardiovascular decision support framework for effective clinical risk assessment

Farooq

Karasek

Atassi

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Healthcare and E-Health (CICARE)

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The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics (RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients with chest pain by helping clinicians effectively distinguish acute angina patients from those with other causes of chest pain. Key to our new approach is (1) an intelligent prospective clinical decision support framework for primary and secondary care clinicians, (2) learning from missing/impartial clinical data using Bernoulli mixture models and Expectation Maximisation (EM) techniques, (3) utilisation of state-of-the-art feature section, pattern recognition and data mining techniques for the development of intelligent risk prediction models for cardiovascular patients. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating clinical 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. A comparative analysis case study of machine learning methods was carried out for predicting RACPC clinical outcomes using real patient data acquired from Raigmore Hospital in Inverness, UK. The proposed framework was also validated using the University of Cleveland's Heart Disease dataset which contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Experiments with the Cleveland database (based on 18 clinical features of 270 patients) were concentrated on attempting to distinguish the presence of heart disease (values 1, 2, 3, 4) from absence (value 0). 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. As part of these case studies, various online RACPC risk assessment prototypes have been developed which are being deployed in the clinical setting (NHS Highland) for clinical trial purposes.

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An Ontology Driven and Bayesian Network Based Cardiovascular Decision Support Framework

Cited by 11 publications

References 12 publications

Using Bayesian networks to identify musculoskeletal symptoms influencing the risk of developing psoriatic arthritis in people with psoriasis

Using Bayesian networks to identify musculoskeletal symptoms influencing the risk of developing psoriatic arthritis in people with psoriasis

An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

A novel cardiovascular decision support framework for effective clinical risk assessment

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