A novel ontology and machine learning driven hybrid clinical decision support framework for cardiovascular preventative care
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| Title: | A novel ontology and machine learning driven hybrid clinical decision support framework for cardiovascular preventative care |
|---|---|
| Authors: | Farooq, Kamran |
| Committee Members: | Hussain, Amir |
| Summary: | Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies. |
| URL: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.668843 |
| Database: | OpenDissertations |
| FullText | Text: Availability: 0 |
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| Header | DbId: ddu DbLabel: OpenDissertations An: ddu.oai.ethos.bl.uk.668843 AccessLevel: 6 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel ontology and machine learning driven hybrid clinical decision support framework for cardiovascular preventative care – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farooq%2C+Kamran%22">Farooq, Kamran</searchLink> – Name: Author Label: Committee Members Group: Au Data: <searchLink fieldCode="CO" term="%22Hussain%2C+Amir%22">Hussain, Amir</searchLink> – Name: Abstract Label: Summary Group: Ab Data: Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies. – Name: URL Label: URL Group: URL Data: <link linkTarget="URL" linkTerm="https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.668843" linkWindow="_blank">https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.668843</link> |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Subjects: – SubjectFull: 616.1 Type: general – SubjectFull: Cardiovascular Clinical Decision Support Framework ; Ontology and Machine Learning Driven Hybrid Clinical Decision Support ; Hybrid Clinical Decision Support Framework ; Clinical medicine--Decision making--Data processing ; Diagnosis--Decision making--Data processing ; Medical informatics Type: general Titles: – TitleFull: A novel ontology and machine learning driven hybrid clinical decision support framework for cardiovascular preventative care Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farooq, Kamran IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2015 |
| ResultId | 1 |
