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A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care - Couverture souple

Farooq, Kamran

 
9783668241992: A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care
Couverture souple
ISBN 10 : 3668241996 ISBN 13 : 9783668241992
Editeur : Grin Verlag, 2016

Synopsis

Doctoral Thesis / Dissertation from the year 2015 in the subject Computer Sciences - Artificial Intelligence, grade: -, University of Stirling (Computing Science and Mathematics), language: English, abstract: 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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Kamran Farooq PhD, is a clinical informatics expert in Stirling, Scotland. He has been working in the research and development field for the past 18 years. His research interests include m-health, computational intelligence in e-health, Big Data, Data Science, Predictive Analytics and gamification in healthcare.

À propos de la quatrième de couverture

Doctoral Thesis / Dissertation from the year 2015 in the subject Computer Sciences - Artificial Intelligence, grade: -, University of Stirling (Computing Science and Mathematics), language: English, abstract: 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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Grin Verlag
Date d'édition
2016
Langue
anglais
ISBN 10 
3668241996
ISBN 13 
9783668241992
Reliure
Broché
Nombre de pages
322
Coordonnées du fabricant
GRIN Publishing GmbH
info@grin.com

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