Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24412
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dc.contributor.authorLEMMENS, Marijn-
dc.contributor.authorTHOELEN, Ronald-
dc.contributor.authorVANDENRYT, Thijs-
dc.contributor.authorDe Raedt, Walter-
dc.contributor.authorGRIETEN, Lars-
dc.date.accessioned2017-09-08T07:05:02Z-
dc.date.available2017-09-08T07:05:02Z-
dc.date.issued2016-
dc.identifier.citationtUL Life Sciences Research Day 2016, Bilzen, Belgium, 05/10/2016-
dc.identifier.urihttp://hdl.handle.net/1942/24412-
dc.description.abstractMonitoring and analysing wounds is still primarily based on visual inspections and interpretations of medical professionals. By maintaining this method there is a high chance of misinterpretation due to diverse ways of examining the wound. Further, for each assessment the wound dressing needs to be removed which results into an intermission of the healing process. The solution to this consists out of two components, firstly the need to monitor the wound going consecutive through each and every wound healing stage is a necessitous part of this research. Besides determining in which stage a wound is in and analyse the behaviour of the wound in each of those stages it is equally important to monitor the conditions in which the wound is healing in. This gives us a two-way monitoring system, on the one hand a long term monitoring system is set-up where the phases of wound healing are the point of focus. On the other hand there is a short term monitoring system, this system gives real-time feedback of critical data on how the wound healing is progressing. In addition to all of the measurement techniques which result into a large dataset, classification and regression machine learning algorithms are used to predict the healing rate of a wound. The goal is to determine a mathematical model, based on existing data as a training set. By looking at patterns of labelled values from the training set different types of machine learning algorithms are used to make statistical reasoned predictions.-
dc.language.isoen-
dc.titleExcluding indecisive decisions by bringing machine learning and Electric Cell-substrate Impedance Sensing (ECIS) together in wound healing-
dc.typeConference Material-
local.bibliographicCitation.conferencedate05/10/2016-
local.bibliographicCitation.conferencenametUL Life Sciences Research Day 2016-
local.bibliographicCitation.conferenceplaceBilzen, Belgium-
local.bibliographicCitation.jcatC2-
local.type.refereedNon-Refereed-
local.type.specifiedPoster-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.contributorVANDENRYT, Thijs-
item.contributorGRIETEN, Lars-
item.contributorTHOELEN, Ronald-
item.contributorLEMMENS, Marijn-
item.contributorDe Raedt, Walter-
item.fullcitationLEMMENS, Marijn; THOELEN, Ronald; VANDENRYT, Thijs; De Raedt, Walter & GRIETEN, Lars (2016) Excluding indecisive decisions by bringing machine learning and Electric Cell-substrate Impedance Sensing (ECIS) together in wound healing. In: tUL Life Sciences Research Day 2016, Bilzen, Belgium, 05/10/2016.-
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