Andere Kunden interessierten sich auch für
![Machine Learning and AI in Clinical Practice]( "Machine Learning and AI in Clinical Practice")
Machine Learning and AI in Clinical Practice12,99 €![Comprehensive Reanalysis of Genomic Storm (Transcriptomic) Data, Integrating Clinical Varibles and Utilizing New and Old Approaches]( "Comprehensive Reanalysis of Genomic Storm (Transcriptomic) Data, Integrating Clinical Varibles and Utilizing New and Old Approaches")
Comprehensive Reanalysis of Genomic Storm (Transcriptomic) Data, Integrating Clinical Varibles and Utilizing New and Old Approaches27,95 €![Novel Approach for Drug Discovery using Neural Network Back Propagation Algorithm]( "Novel Approach for Drug Discovery using Neural Network Back Propagation Algorithm")
Novel Approach for Drug Discovery using Neural Network Back Propagation Algorithm27,95 €![Approaches and Applications of Deep Learning in Virtual Medical Care]( "Approaches and Applications of Deep Learning in Virtual Medical Care")
Approaches and Applications of Deep Learning in Virtual Medical Care343,99 €![Theory and Novel Applications of Machine Learning]( "Theory and Novel Applications of Machine Learning")
Theory and Novel Applications of Machine Learning102,99 €![In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants]( "In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants")
In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants17,95 €![Health Care in the Information Society]( "Health Care in the Information Society")
Health Care in the Information Society140,99 €
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.