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Monitoring Gestational Diabetes
Applied Intelligent Systems Lab
29th of November 2012 – TheArk eHealth event
aislab.hevs.ch
Dr. Stefano Bromuri, Johannes Krampf, Dr René Schumann,
Prof. Dr. Michael Schumacher
G-DEMANDE: Monitoring
Gestational Diabetes Mellitus

           o  Gestational diabetes mellitus (GDM) is a
               condition that affects 2-5% of all the
               pregnancies and manifests itself with
               high blood sugar levels during
               pregnancy.
           o  It can cause health problems for mother
               and child like,
              n    Preeclampsia, Eclampsia,
              n    Hyperglycemia,
              n    Macrosomia, and
              n    Diabetes type II

                                                          2
Problems in current Care


1.  Current treatment practices:
    Doctors usually see patient 2-3
    times a week. In case of
    hyperglycemia, the patient may
    arrive too late.
2.  At each patient visit, the doctor
    sees the glucose levels and blood
    pressure at the very moment of the
    consultancy, and not the evolution
    of the values.



                                         3
Mobile data acquisition




                          4
Pervasive Health System

o  Incoming physiological patient data is
   a)  Stored in a database
   b)  Forwarded to an expert system analyzing the data




                                                          5
Expert System (Golem)


o  An expert agent is monitoring
    the condition of one patient…
o  … and reasoning on its values

o  To scale-up the agents can be:
   n  distributed to different nodes;
   n  activated and deactivated by a
       balancer;
   n  persisted in an agent database.



                                         6
Medical knowledge representation
                             Abductive Rules Example




        Described rules
        (abductive &
        deductive) for the
        agents.




                                                       7
8
Feasibility tests within CHUV hospital
o  Started in November 2012 for a period of 6-9 months
    with 12 patients




                                                          12
Applied Intelligent Systems Lab (2009-2012)




o    Chronic disease monitoring
o    eHealth interoperability standards
o    Medical data analysis
o    Medical decision support
o    (in the future: Healthcare coordination)
                                                 13

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Monitoring Gestational Diabetes

  • 1. Monitoring Gestational Diabetes Applied Intelligent Systems Lab 29th of November 2012 – TheArk eHealth event aislab.hevs.ch Dr. Stefano Bromuri, Johannes Krampf, Dr René Schumann, Prof. Dr. Michael Schumacher
  • 2. G-DEMANDE: Monitoring Gestational Diabetes Mellitus o  Gestational diabetes mellitus (GDM) is a condition that affects 2-5% of all the pregnancies and manifests itself with high blood sugar levels during pregnancy. o  It can cause health problems for mother and child like, n  Preeclampsia, Eclampsia, n  Hyperglycemia, n  Macrosomia, and n  Diabetes type II 2
  • 3. Problems in current Care 1.  Current treatment practices: Doctors usually see patient 2-3 times a week. In case of hyperglycemia, the patient may arrive too late. 2.  At each patient visit, the doctor sees the glucose levels and blood pressure at the very moment of the consultancy, and not the evolution of the values. 3
  • 5. Pervasive Health System o  Incoming physiological patient data is a)  Stored in a database b)  Forwarded to an expert system analyzing the data 5
  • 6. Expert System (Golem) o  An expert agent is monitoring the condition of one patient… o  … and reasoning on its values o  To scale-up the agents can be: n  distributed to different nodes; n  activated and deactivated by a balancer; n  persisted in an agent database. 6
  • 7. Medical knowledge representation Abductive Rules Example Described rules (abductive & deductive) for the agents. 7
  • 8. 8
  • 9.
  • 10.
  • 11.
  • 12. Feasibility tests within CHUV hospital o  Started in November 2012 for a period of 6-9 months with 12 patients 12
  • 13. Applied Intelligent Systems Lab (2009-2012) o  Chronic disease monitoring o  eHealth interoperability standards o  Medical data analysis o  Medical decision support o  (in the future: Healthcare coordination) 13