Predicting clinical status of patients after an acute ischemic stroke using random forests
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238451" target="_blank" >RIV/61989100:27240/17:10238451 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/abstract/document/8024330/" target="_blank" >http://ieeexplore.ieee.org/abstract/document/8024330/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/DT.2017.8024330" target="_blank" >10.1109/DT.2017.8024330</a>
Alternative languages
Result language
angličtina
Original language name
Predicting clinical status of patients after an acute ischemic stroke using random forests
Original language description
According to the World Health Organization, a stroke has been the second most common cause of death in the world in the last 15 years. An ischemic stroke accounts for almost 80 % of all cases. The University Hospital Ostrava in the Czech Republic collects various information about patients who were transported there after suffering from an acute ischemic stroke, such as the affected brain hemisphere, duration of medical procedure or presence of hypertension. The objective of this paper was finding a model which would be able to predict patient's clinical outcome three months after an ischemic stroke based on the collected data. It was also desirable to analyse importance of the considered variables. For this purpose, the random forests algorithm was used. To avoid biased variable importance, we used an alternative approach to the random forests which uses the conditional inference trees. Firstly, the commonly used modified Rankin Scale was used for describing the patient's outcome three months after a stroke. Secondly, only two values for the clinical status were considered, by meaning they correspond with the values 0-3 and 4-6 of modified Rankin Scale. The best performance was achieved with the second approach to description of the clinical outcome with the calculated classification accuracy 86 %. © 2017 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the International Conference on Information and Digital Technologies, IDT 2017
ISBN
978-1-5090-5688-0
ISSN
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e-ISSN
neuvedeno
Number of pages
6
Pages from-to
417-422
Publisher name
IEEE
Place of publication
Piscataway
Event location
Žilina
Event date
Jul 5, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000426916900066