Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/62183
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dc.contributor.authorTay, Darwin Jia Xianen
dc.date.accessioned2015-02-25T02:42:59Zen
dc.date.available2015-02-25T02:42:59Zen
dc.date.copyright2015en
dc.date.issued2015en
dc.identifier.citationTay, D. J. X. (2015). Decision support continuum paradigm for cardiovascular disease : towards personalized predictive models. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/62183en
dc.description.abstractClinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.en
dc.format.extent261 p.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Bioengineeringen
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen
dc.titleDecision support continuum paradigm for cardiovascular disease : towards personalized predictive modelsen
dc.typeThesisen
dc.contributor.supervisorPoh Chueh Looen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen
dc.description.degreeBIOENGINEERINGen
dc.contributor.organizationImperial College Londonen
dc.identifier.doi10.32657/10356/62183en
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