{"id":91,"date":"2014-10-30T16:02:06","date_gmt":"2014-10-30T15:02:06","guid":{"rendered":"http:\/\/iui.sb.dfki.de\/smartfactories\/www\/?page_id=91"},"modified":"2020-11-13T10:09:21","modified_gmt":"2020-11-13T10:09:21","slug":"consortium","status":"publish","type":"page","link":"https:\/\/medicalcps.dfki.de\/?page_id=91","title":{"rendered":"MCPS"},"content":{"rendered":"\n

Funding period 01\/2013 – 12\/2014<\/p>\n\n\n

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“This activity is the first attempt of finding a structure of reason or rationality in a complex medical decision problem by taking both active input data and passive sensory data into account—a knowledge which is, under normal circumstances, hidden in the heads of the medical experts. All EIT tasks ought to nourish this central idea of a medical CPS.” (Daniel Sonntag, DFKI)<\/p>\n

THESEUS Medico Carrier Vision (Flash)<\/h2>\n
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THESEUS Medico Carrier Vision (Flash)<\/p>\n<\/div>\n\n\n

Real-Time Multimedia Information Extraction Scenario<\/h2>\n\n\n\n
\"EIT<\/figure>\n\n\n\n

Objectives<\/h2>\n\n\n\n

We will combine active and passive user input modes in clinical environments for knowledge discovery and knowledge acquisition toward decision support in clinical environments<\/strong>. Active input modes include digital pens, smartphones, and automatic handwriting recognition for a direct digitization of patient data. Passive input modes include sensors of the clinical environment and\/or mobile smartphones. This combination for knowledge acquisition and decision support (while using machine learning techniques) has not yet been explored in clinical environments and is of specific interest because it combines previously unconnected information sources. The innovative aspect is a holistic view on individual patients whereby individual active and passive patient data can be taken into account for clinical decision support.<\/p>\n\n\n\n

Expected Outcomes<\/h2>\n\n\n\n