Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Discriminative Prediction of Adverse Events for Optimized Therapies Following Traumatic Brain Injury
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0003-1654-9148
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Traumatic brain injury (TBI) causes temporary or perma- nent alteration in brain functions. At intensive care units, TBI patients are usually multimodally monitored, thus rendering large volumes of data on many physiological variables. For the physician, these data are difficult to interpret due to their complexity, speed and volume. Thus, computa- tional aids are recommended, e.g., for predicting patient’s clinical status in near future. In this article, we describe a probabilistic model that can be used for aiding physician’s decision making process in TBI patient care in real time. Our model tries to capture time varying patterns of patient’s clinical information. The model is built by using a discrimi- native model learning framework so that it can predict adverse clinical events with a higher level of accuracy. That is, our model is built so that prediction of certain desired events are given more attention than that of the other less important ones. This can be achieved by estimating model parameters in such a way, for e.g. using a cost function, when a suitable model structure has been selected, that again can be done dis- criminatively. However, such estimation procedures have no closed form solutions, so numerical optimization methods are used.

Place, publisher, year, edition, pages
Umeå, 2019. article id 3
Keywords [en]
Dependence, Accuracy, Clinical, Real time
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-160522OAI: oai:DiVA.org:umu-160522DiVA, id: diva2:1327497
Conference
31st Swedish AI Society Workshop 2019, June 18–19, 2019, Umeå, Sweden
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-20Bibliographically approved

Open Access in DiVA

fulltext(281 kB)15 downloads
File information
File name FULLTEXT01.pdfFile size 281 kBChecksum SHA-512
cad3c2e9554cd5bfb2836b5c5c55c0801ee9c9271d5554cc859f744f830bc6714afda5da9055a480c474f5d27c334594e65d330e5d0d89ff39c683ce0f8cc810
Type fulltextMimetype application/pdf

Other links

URL

Search in DiVA

By author/editor
Wijayatunga, PriyanthaSundström, Nina
By organisation
StatisticsDepartment of Radiation Sciences
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 15 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 91 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf