Predicting clinical status of patients after an acute ischemic stroke using random forests

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting clinical status of patients after an acute ischemic stroke using random forests

Popis výsledku v původním jazyce

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&apos;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&apos;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.

Název v anglickém jazyce

Predicting clinical status of patients after an acute ischemic stroke using random forests

Popis výsledku anglicky

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&apos;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&apos;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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the International Conference on Information and Digital Technologies, IDT 2017

ISBN

978-1-5090-5688-0

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

417-422

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Žilina

Datum konání akce

5. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426916900066