Early recognition of depression at diabetes mellitus with the cognitive medical system COMES®

Thomas Spittler, Michael Handwerker, Petra Friedrich, Bernhard Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Studies show that diabetes mellitus type 2 can cause depression but it can also be activated by depression. That can be favored by various psycho-social factors as anxiety or stress. With the new telemedical assistance system COMES® an experiment shall be conducted that tries to implement a risk classification by means of data mining algorithms. The classification model on the one hand provides a conclusion regarding the risk of depression at illness of diabetes mellitus and on the other hand expresses the possibility of contracting diabetes mellitus at diagnosis of depression. Additionally, important physiological data for diabetes mellitus and its secondary diseases as blood pressure, weight and activity can be updated with the COMES® system every day. From the gained expertise preventive therapeutic measures can be implemented. Hence, COMES® aims actively to make a contribution to alleviate diabetes mellitus and identify the risk of depression precociously as well as to take antagonizing therapeutic measures.

Original languageEnglish
Title of host publicationProceedings of the IADIS International Conference WWW/Internet 2011, ICWI 2011
EditorsLuis Rodrigues, Bebo White, Pedro Isaias, Flavia Maria Santoro
PublisherIADIS
Pages505-508
Number of pages4
ISBN (Electronic)9789898533012
StatePublished - 2011
EventIADIS International Conference WWW/Internet 2011, ICWI 2011 - Rio de Janeiro, Brazil
Duration: 5 Nov 20118 Nov 2011

Publication series

NameProceedings of the IADIS International Conference WWW/Internet 2011, ICWI 2011

Conference

ConferenceIADIS International Conference WWW/Internet 2011, ICWI 2011
Country/TerritoryBrazil
CityRio de Janeiro
Period5/11/118/11/11

Keywords

  • Cognitive systems
  • Depression
  • Diabetes
  • Personalized medicine
  • Risk classification
  • Telemedical intervention

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