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Title: Data mining and associated analytical tools as decision aids for healthcare practitioners in vascular surgery
Author: Mofidi, Reza
ISNI:       0000 0004 7226 497X
Awarding Body: University of Sunderland
Current Institution: University of Sunderland
Date of Award: 2018
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Vascular surgery is an increasingly data rich speciality. Planning treatment and assessing outcomes are highly dependent on objective assessment of number of imaging modalities including duplex ultrasound, CT scans and angiograms which are almost exclusively digitally created stored and accessed. Developments such as the national vascular registry mean that treatment outcomes are recorded scrutinised electronically. The widespread availability of data which is collected electronically and stored for future clinical use has created the opportunity to examine the efficacy of investigations and treatments in a way which has hitherto not been possible. In addition, new computational methods for data analysis have provided the opportunity for the clinicians and researchers to utilise this data to address pertinent clinical questions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Health Sciences