Disputas - Master of Science Ashenafi Zebene Woldaregay

Master of Science Ashenafi Zebene Woldaregay will Friday May 28th at 12:15 publically defend his thesis for the PhD degree in Science

Title of the thesis:

«EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes»

 

Popular scientific abstract:

New Frontiers for Infectious Disease Surveillance: Watching the Individual’s Health Status 24/7 Through Self-Generated Health-Related Data 

Imagine a personalized health model that computes your health status in real-time through data generated from your wearables and automatically detects when you are infected just by looking at the data. Early warning information from this kind of individualized model can be beneficial for you as well as for other people (the general public); 1) it can help you to be aware of any potential health changes and 2) it can be used to detect infectious disease outbreaks within the city, and such outbreak information can be useful for you as well as for other peoples in the city to avoid being infected. This is exactly what the Ph.D. research of Ashenafi Zebene Woldaregay looks into considering people with type 1 diabetes as a case and harnesses self-recorded data from this group of peoples to realize a personalized health model-based tool.     

As we all know, the current tragic event due to the corona-virus (SARS-CoV-2) outbreak, also known as COVID-19, has resulted in mass panic among us and the loss of many lives around the globe. One thing for sure, among many other things, the incident has thought us that we as a society stand far from prepared for such kind of outbreaks. To better cope with similar or even worse infectious disease outbreaks in the future, we need a well-equipped early outbreak detection system. In this regard, harnessing self-generated data and developing a personalized health model is crucial to meet the demand for early detection. Apart from early outbreak detection, as we witnessed during the COVID-19 outbreak, one of the challenges was identifying infected individuals from the community, and in this regard, you can imagine how beneficial it is to have a personalized health model that can enable us to track the health status of the individual citizen in real-time.

My Ph.D. research investigated the possibilities and developed a personalized health model for individuals with type 1 diabetes. To begin with, there is evidence that reveals the influence of infection on the individual blood glucose hemostasis. Those who have this as a chronic condition, lack insulin secretion within the body and need to inject exogenous insulin, estimate carbs in the diet, and perform balanced physical activity to control their blood glucose levels. As part of self-management practice, they usually record these pieces of information to be able to achieve a healthy blood glucose level. Therefore, my Ph.D. research capitalizes on these self-recorded data and put forward solutions. Further, it also looks into challenges such as user privacy, security, confidentiality, and other similar concerns that arise during implementation.

The thesis is published in Munin and is available at: https://hdl.handle.net/10037/21149

 

Supervisors:

  • Professor Gunnar Hartvigsen, Department of Computer Science, UiT (main supervisor)
  • Professor Eirik Årsand, Department of Computer Science, UiT
  • Assistant Professor Taxiarchis Botsis, The John Jopkins University School of Medicine, USA

 

Evaluation committee:

  • Professor Rune Fensli, Department of Information and Communication Technology, Faculty of Engineering and Science, Universitetet i Agder (1. opponent)
  • Associate Professor Mette Dencker Johansen, Department of Health Science and Technology, The Faculty of Medicine, Aalborg Universitet (2. opponent) 
  • Associate Professor Håvard D. Johansen, Department of Computer Science, UiT (internal member and leader of the committee)

The 1st and 2nd opponent will participate remotely to the defence.

 

Leader of the public defense:
The leader of the public defense is Professor Anders Andersen, Head of the Department of Computer Science, Faculty of Science and Technology, UiT.

 

Opposition ex auditorio:
If you have any questions for the candidate during the public defence, please send an e-mail to the leader of the public defence. They will announce the questions during the defence.

 

Trial lecture:

The trial lecture is held Friday May 28th at 10:15 digitally.

Title of the trial lecture: «Challenges, regulations, and solutions for data sharing in health care»

 

Streaming:

The defense and trial lecture will be streamed via Mediasite: 

https://mediasite.uit.no/Mediasite/Catalog/Full/b027e1abd75e42b8ac4cf6849605497821

 

Audience:

UiT follows the national guidelines regarding infection control. A maximum of 20 people are allowed in the auditorium during the defence, as long as everybody keeps a distance of 1 meter at all times.

 

When: 28. May 2021 kl. 12.15–15.00
Where: Teknologibygget Auditorium 1.022
Location / Campus: Tromsø
Target group: Ansatte, Studenter, Besøkende, Inviterte
Contact: Jakob Holden Hansen
E-boastta: Jakob.h.hansen@uit.no
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