Towards intelligent diabetes knowledge management and knowledge discovery : a data mining approach

Darwish, F

Abstract

Self-monitoring and self-management play an increasingly vital role in the management of prevalent diseases afflicting millions of people worldwide. Conditions such as cardiovascular disease or diabetes mellitus can be managed with pharmaceuticals. However, lifestyle factors and behaviour modifications also play a crucial part in controlling outcomes. If carried out effectively, self-monitoring and management techniques can benefit the patient from a health point of view, empowering them to take control of their disease, and also support the health sector economically. A recent report showed that almost four fifths of the NHS diabetes budget was spent on managing preventable complications.
Although applicable to any medical conditions with lifestyle control elements, this thesis will be using diabetes mellitus as a recurring theme to highlight research conducted. Diabetes is a rapidly growing epidemic disease with global implications impacting humans socially and economically. By 2025 it is estimated that five million people in the UK will be diagnosed with diabetes., This research will therefore, be relevant for a large proportion of the population.
Knowledge Management (KM) has demonstrated to be a valuable approach to sharing knowledge and providing users with the information necessary to help self-manage their symptoms. Although, KM has not yet been applied sufficiently to support the growing number of diabetics in the UK. In this thesis, KM is merged with Knowledge Discovery (KD) to combat that and address the specific needs of the diabetic population. The integrated framework is implemented using data mining techniques within the proposed e-Toolkit to elicit useful knowledge encountered by patients regardless of their disease, such as adverse drug reactions. The knowledge is then disseminated through the proposed modified SECI Model for knowledge creation via the e-Toolkit.
The second part of this research investigates which patient data is necessary to disseminate to healthcare professionals. This help to bridge any communications gap that may exist between patients and health care professionals. In theory, the e-toolkit will provide patients with one place to record every important health factor, whilst simultaneously allowing the medical team with real-time monitoring. Thus, enabling doctors and patients to work together to find effective ways to reduce the damaging effects of this disease, including determining the common side effects through medication reviews.
Keywords: Diabetes, Knowledge Management, Knowledge Discovery, British National Health Service Web System, Doctor, Patient, Data Mining.

Citation

Darwish, F. Towards intelligent diabetes knowledge management and knowledge discovery : a data mining approach. (Dissertation). University of Salford

• Thesis Type: Dissertation
• Deposit Date: Dec 24, 2019
• Publicly Available Date: Dec 24, 2019
• Award Date: Jan 1, 2019