Header image.

EDMON

Brief project description

Assume that you are the mayor in a large city, and ask “the mirror” in the morning: How are we today? (“we” as for the whole population in your city). “The mirror” then visualizes the health status for the citizens based on data from EDMON, the Electronic Disease Surveillance Monitoring Network. EDMON provides information about the spread of contagious diseases, air quality, pollution, and other factors affecting peoples’ health, particularly people suffering from noncommunicable diseases. The information is made available to all citizens. If you suffer from COPD, you need to know whether the air quality in your neighborhood is good or not. If you are a person with diabetes, you want to know whether the risk of infections has increased, and if possible, in which areas of the city. Such information is important for all people with weakened immune system or poor health and can be provided by EDMON. EDMON  will use techniques from big data analytics, social media, mobile computing and a novel health-monitoring system. The first version of EDMON will focus on people with diabetes.

To our knowledge, disease surveillance systems for the detection of a contagious disease at a very early stage, i.e., within hours after the first persons in a population have been infected, do not exist. Examples like Google Flu or other systems based on real-time data have not been designed to support such activities and they are mainly designed to detect an epidemic. Healthmap is one other example of software that mines websites, social networks and local news reports to map potential disease outbreaks — still based on data following the onset of the first symptoms.

In certain cases EDMON might be able to issue a warning 1-2 weeks ahead of the earliest point of detection by today’s systems. For many patient groups as well as for health professionals, this information is highly appreciated. To be successful, systems like EDMON require (1) access to a large cohort of people who frequently and regularly record their own health data and make them available for secondary use, (2) good understanding of the complex physiological nature of the human body and the different effects caused by pathogens, and (3) computational models for the identification of deviations from expected values. If successful, our project will form the basis for a new direction in the area of disease surveillance.

In the ‘Electronic Disease Surveillance Monitoring Network (EDMON)’ project we address the following objectives:

(O1)    Establish an appropriate distributed system architecture for an Electronic Disease Surveillance Monitoring Network that incorporates health-related data from people with chronic and noncommunicable diseases (i.e., diabetes in the first version);

(O2)    Identify appropriate equipment and develop modules to collect accurate physiological and physical data from people with chronic and noncommunicable diseases (i.e., diabetes: blood glucose values, infection-related data, body thermometers, insulin and food intake, physical activity);

(O3)   Develop dedicated mathematical models that will process the incoming data to facilitate the early detection of infections, i.e., for some people, before the onset of the first symptoms; and,

(O4)    Produce notifications or alerts according to the processing output from the Electronic Disease Surveillance Monitoring Network.

In the EDMON system, the data collection will be accomplished through the use of small medical devices, e.g., blood glucose monitors, body thermometers, physical activity sensors (as FitBit), etc., which will measure and automatically transmit physiology data to the receptor. A smart phone with the dedicated software will be the receptor. The recorded data will then be transmitted to a cloud-based service for further processing. The incoming data will feed dedicated mathematical models that will analyze the input and produce alerts when an anomaly is detected. Thus, our project besides its ICT value will have a clear benefit for society. It should also be mentioned here that data security issues and privacy protection will be ensured in all system processes and will agree with the National and International Ethical Guidelines.

Project group

Project leader:
Professor Gunnar Hartvigsen (UiT)

Project members:
Research Scholar Ashenafi Zebene Woldaregay (UiT)
Research scholar Andre Henriksen (UiT)
Research scholar Anna Holubová (Department of ICT in Medicine, Faculty of Biomedical Engineering, Czech Technical University, Prague, Czech Republic)
Professor Eirik Årsand (UiT/NSE,UNN)
Professor Keiichi Sato (IIT, Chicago)
Assoc. professor Santiago Martinez (UiA)
Assoc. professor Jorge Igual Garcia  (UPV, Valencia, Spain)
Ass. professor Taxiarchis Botsis (The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA)
Dr. David Albers (Department of Biomedical Informatics, Columbia University, N.Y., USA)
Dr. Antonio Martinez Millana (UPV, Valencia, Spain)
Postdoc Ilkka Kalervo Launonen (UiT)

MSc students:

 

Publications (selected)

Journal Papers:

  1. Arsand E, Walseth OA, Andersson N, Fernando R, Granberg O, Bellika JG, Hartvigsen G. Using blood glucose data as an indicator for epidemic disease outbreaks. Studies in Health Technology and Informatics, 2005;116:217–22. PMID: 16160262
  2. Bellika, J.G., Hasvold, T., Hartvigsen, G. Propagation of program control: A tool for distributed disease surveillance. International Journal of Medical Informatics. 2007 April:76:4:313–329. PMID: 16621681
  3. Bellika, J.G., Sue, H., Bird, L., Goodchild, A., Hasvold, T., Hartvigsen, G. Properties of a Federated Epidemiology Query System. International Journal of Medical Informatics. 2007, 76(9):664–676. PMID: 16949338
  4. Granberg, O., Bellika, JG., Årsand, E., Hartvigsen, G. Automatic Infection Detection System. Studies in Health Technology and Informatics, 2007;129:566–570. PMID: 17911780
  5. Vuurden, K. van, Hartvigsen, G., Bellika, J.G. Disease outbreak detection through clique covering on a weighted ICPC-coded graph. Studies in Health Technology and Informatics, 2008;136: 271–276. PMID: 18487743
  6. Botsis, T., Hejlesen, O., Bellika, J.G., Hartvigsen, G. Electronic disease surveillance for sensitive population groups – The diabetics case study. Studies in Health Technology and Informatics, 2008;136: 365–370. PMID: 18487758
  7. Johansen, M.A., Scholl, J., Aronsen, G., Hartvigsen, G., Bellika, J.G. En Exploratory Study of Disease Surveillance Systems in Norway. Journal and telemedicine and telecare. 2008: 14(7):368–371. PMID: 18852319
  8. Hartvigsen, G., Årsand, E., Botsis, T., van Vuurden, K., Johansen, M., Bellika, J.G. (2009). Reusing Patient Data to Enhance Patient Empowerment and Electronic Disease Surveillance. The Journal on Information Technology in Healthcare, 7, Issue: 1 (February 2009), pp. 4–12.
  9. Botsis, T., Walderhaug, S., Dias, A., Vuurden, K. van, Bellika, J.G., Hartvigsen, G. Point-of-care devices for healthy consumers – a feasibility study. Journal and telemedicine and telecare 2009; 15(8): 419–420. PMID: 19948710
  10. Botsis, T., Hartvigsen, G. Exploring new directions in disease surveillance for people with diabetes: Lessons learned and future plans. Studies in Health Technology and Informatics, 2010;160: 466–470. PMID: 20841730
  11. Botsis, T., Lai, A.M., Palmas, W., Starren, J.B., Hartvigsen, G., Hripcsak, G. Proof of concept for the role of glycemic control in the early detection of infections in diabetics. Health Informatics Journal, 2012, 18(1), 26–35. PMID: 22447875
  12. Woldaregay, A.Z., Årsand, E., Botsis, T., Hartvigsen, G. An Early Infectious Disease Outbreak Detection Mechanism Based on Self-Recorded Data from People with Diabetes. Studies in Health Technology and Informatics, 2017;245:619–623. PMID: 29295170
  13. Woldaregay, A.Z., Issom, D.-Z., Henriksen, A., Marttila, H., Mikalsen, M., Pfuhl, G., Sato, K., Lovis, C., Hartvigsen, G. Motivational Factors for User Engagement with mHealth Apps. Studies in Health Technology and Informatics, 2018;249:151–157. PMID: 29866972
  14. Woldaregay, A.Z., Årsand,, Botsis, T., Albers, D., Mamykina, L., Hartvigsen, G. Data Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine Learning Applications in Type 1 Diabetes. J Med Internet Res 2019;21(5):e11030. PMID:31042157
  15. Woldaregay, A.Z., Årsand,, Walderhaug, S., Albers, D., Mamykina, L., Botsis, T., Hartvigsen, G. Data-driven modelling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artificial Intelligence in Medicine 2019;98(July): 109-134. PMID: 31383477

International Conference Papers:

  1. Johansen, M.A., Scholl, J., Aronsen, G., Hartvigsen, G., Bellika, J.G. An Exploratory Study of Disease Surveillance Systems in Norway. Tromsø Telemedicine and eHealth Conference (TTeC 2008) (Tromsø, Norway, 9–11 June 2008).
  2. Hartvigsen, G., Årsand, E., Botsis, T., van Vuurden, K., Johansen, M., Bellika, J.G Improved patient empowerment and continuity of care through electronic disease surveillance. ICICT 2008 (Samos, Greece, 11–13 July 2008). Publ.: National and Kapodistrian University of Athens, Greece, pp. 230–235. (ISBN: 978-960-466-013-1  ISSN: 179-3904)
  3. Botsis, T., Bellika, J.G., Hartvigsen, G. New Directions in Electronic Disease Surveillance: Detection of Infectious Diseases during the Incubation Period. Proceedings of International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED 2009). IEEE Computer Society, 2009, pp. 176–183. (ISBN 978-0-7695-3532-6)
  4. Lauritzen, J., Årsand, E., Van Vuurden, K., Bellika, J.G., Hejlesen, O.K, Hartvigsen, G. Towards a Mobile Solution for Predicting Illness in Type 1 Diabetes Mellitus 2nd International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems :: Wireless VITAE 2011 (28 February – 3 March 2011, Chennai, India). IEEE Press. (ISBN 978-1-4577-0786-5)
  5. Van Vuurden, K., Bassøe, C.-F., Hartvigsen, G. Outbreak detection based on a tree-structured anatomic model for infection. In: Karlsson, D., Elberg, P.B., Fossum, M., Galster, G., Hartvigsen, G., Koch, S., Nilsson, G. (Eds.). Scandinavian Conference on Health Informatics 2012. Linköping Electronic Conference Proceedings, No. 70.  Linköping, Sweden: Linköping University Electronic Press, 2012, pp. 35–39. (ISSN: 1650-3686 (print) ISSN: 1650-3740 (online) ISBN: 978-91-7519-758-6)
  6. Woldaregay, A.Z., Vuurden, K van, Årsand, E., Botsis, T., Hartvigsen, G., Electronic Disease Surveillance System Based on Input from People with Diabetes: An Early Outbreak Detection Mechanism. In: Karlsson, D., Budrionis, A., Bygholm, A., Fossum, M., Granja, C., Hartvigsen, G., Hejlesen, O., Hägglund, M., Johansen, M.A., Lindsköld, L., Martinez, S., Moe, C.E., Ruiz, L.M., Vimarlund, V., Yigzaw, K.Y. (Eds.). “SHI 2016. Proceedings of the 14th Scandinavian Conference on Health Informatics”. (6–7 April 2016, Gothenburg, Sweden). Linköping Electronic Conference Proceedings, No. 122. Linköping, Sweden: Linköping University Electronic Press, 2016, pp. 23–27. (ISSN: 1650-3686 (print) ISSN: 1650-3740 (online) ISBN: 978-91-7685-776-2)
  7. Woldaregay, A.Z., Årsand, E., Giordanengo, A., Albers, D., Mamykina, L., Botsis, T., Hartvigsen, G. EDMON – A Wireless Communication Platform for a Real-Time Infectious Disease Outbreak Detection System Using Self-Recorded Data from People with Type 1 Diabetes. In: Martinez, S., Budrionis, A., Bygholm, A., Fossum, M., Hartvigsen, G., Hägglund, M., Moe, C.E., Thygesen, E., Vimarlund, V., Yigzaw, K.Y. (Eds.). Proceedings from the 15th Scandinavian Conference on Health Informatics 2017. Linköping Electronic Conference Proceedings, No. 145. Linköping, Sweden: Linköping University Electronic Press, 2017, pp. 14-20. (ISSN: 1650-3686 (print) ISSN: 1650-3740 (online) ISBN: 978-91-7685-364-1)
  8. Yeng, P.K., Woldaregay, A.Z., Solvoll, T., Hartvigsen, G. A Systematic Review of Cluster Detection Mechanisms in Syndromic Surveillance: Towards Developing a Framework of Cluster Detection Mechanisms for the EDMON system. In: Bygholm, A., Pape-Haugaard, L., Niss, K., Hejlesen, O., Zhou, C. (Eds.). Proceedings from the 16th Scandinavian Conference on Health Informatics 2018. Linköping Electronic Conference Proceedings, No. 151. Linköping, Sweden: Linköping University Electronic Press, 2018, pp. 62-69. (ISSN: 1650-3686 (print)
 ISSN: 1650-3740 (online)
 ISBN: 978-91-7685-213-2)
  9. Coucheron, S., Woldaregay, A.Z., Årsand, E., Botsis, T., Hartvigsen, G. EDMON – A System Architecture for Real-Time Infection Monitoring and Outbreak Detection Based on Self-Recorded Data from People with Type 1 Diabetes: System Design and Prototype Implementation. In: Granja, C., Solvoll, T. (Eds.). SHI 2019: Proceedings of the 17th Scandinavian Conference on Health Informatics. Linköping Electronic Conference Proceedings, No. 161. Linköping, Sweden: Linköping University Electronic Press, 2019, pp. 37-44. (ISSN: 1650-3686 (print) eISSN: 1650-3740 (online) ISBN: 978-91-7929-957-6)
  10. Yeng,K., Woldaregay, A.Z., Harvigsen, G.  K-CUSUM: Cluster Detection Mechanism in EDMON. In: Granja, C., Solvoll, T. (Eds.). SHI 2019: Proceedings of the 17th Scandinavian Conference on Health Informatics. Linköping Electronic Conference Proceedings, No. 161. Linköping, Sweden: Linköping University Electronic Press, 2019, pp. 141-147. (ISSN: 1650-3686 (print)eISSN: 1650-3740 (online) ISBN: 978-91-7929-957-6)
Created on 18/03/2019 by:

Categories:

Comments are closed.