Ontology-Driven Cardiovascular Decision Support System

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

doi:10.4108/icst.pervasivehealth.2011.246092

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…Farooq et al [26] propose an Ontology-Based approach to design a Rule-Based CDSS for chest pain risk assessment. The patient data (cardiovascular history and family History) is collected through a questionnaire-ontology as per the recommendations of NICE 1 guidelines.…”

Section: A Rule-based Cdsssmentioning

confidence: 99%

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Benmimoune

Hassani

Ghodous

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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Clinical decision support systems (CDSSs) have been hailed for their potential to reduce medical errors and improve the quality and safety of health care. CDSSs aim to assist clinicians in their decision making and take over some routines including diagnostic support. This paper presents a design of a generic medical platform to assist clinicians in their clinical reasoning process. Our platform aims to guide clinicians in the process of gathering relevant information and to help them in their decision making. It exploits two sources of knowledge namely: theoretical knowledge (clinical rules) and practical knowledge (clinical experiences) by relying on RuleBased Reasoning and Case-Based Reasoning, respectively. moreover, we utilize the semantic web and ontologies for knowledge modeling in order to reuse and to share knowledge.

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Section: A Rule-based Cdsssmentioning

confidence: 99%

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Benmimoune

Hassani

Ghodous

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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“…The respiratory system ontology [43] has developed with the following classes and subclasses. The parent root node class is Respiratory diseases with connected sub classes: pneumoconiosis and other lung diseases due to external agents, Acute Respiratory infection, chronic obstructive pulmonary disease and allied conditions etc are the sub classes of the parent node class and further these sub classes have connected with other sub classes to return query results.…”

Section: Respiratory Disease Ontologymentioning

confidence: 99%

“… This work presents respiratory disease related issues how these kinds of information can gather from scattered environment. For this purpose, authors have discussed architecture of semantic health information query [43] with various reasoning rules along with ontology concepts. So respiratory system ontology has been developed with the following classes and subclasses.…”

Section: B Specific Medical Ontologiesmentioning

confidence: 99%

Using Ontologies for the Emergency Response Support: A Survey

Ullah

Khan

2015

VAWKUM trans. comput. sci.

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Ontology is an efficient and an adequate way of conceptualization of knowledge representation for real world applications. Ontologies are successfully used in varieties of knowledge domains. In this paper, we focus on the field of medical ontology knowledge construction and representation. We identified three different medical ontologies classes: Generic, Specific and Mass Casualties Incidents (MCI) medical ontologies. This survey should support the construction of medical Ontologies in future for a better patient treatment. In particular, we survey and focus on the existing medical related ontologies and investigate their suitability for use in Mass Casualties Incidents (MCI).

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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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Ontology-Driven Cardiovascular Decision Support System

Cited by 16 publications

References 15 publications

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Using Ontologies for the Emergency Response Support: A Survey

A novel cardiovascular decision support framework for effective clinical risk assessment

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