D 2015

Analysis of Neurosurgery Data Using Statistical and Data Mining Methods

BERKA, Petr, J. JABLONSKÝ, Luboš MAREK a Michal VRABEC

Základní údaje

Originální název

Analysis of Neurosurgery Data Using Statistical and Data Mining Methods

Autoři

BERKA, Petr (203 Česká republika, garant, domácí), J. JABLONSKÝ (203 Česká republika), Luboš MAREK (203 Česká republika) a Michal VRABEC (203 Česká republika)

Vydání

9414. vyd. Switzerland, Advances in Artificial Intelligence and Its Applications. 14th. Mexican Int. Conference on Artificial Intelligence, od s. 310-321, 12 s. 2015

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/04274644:_____/15:#0000087

Organizační jednotka

Vysoká škola finanční a správní

ISBN

978-3-319-27100-2

ISSN

DOI

UT WoS

000367681400023

Klíčová slova anglicky

Engineering controlled terms; Artificial intelligence; Decision trees; Neurosurgery; Patient monitoring; Patient treatment; Surgery; Trees (mathematics)

Štítky

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 29. 3. 2017 14:52, Ing. Dominika Moravcová

Anotace

V originále

The data concerning the outcomes of surgical clipping and endovascular treatment in acute aneurysmal subarachnoid hemorrhage (SAH) patients have been analyzed to reveal relations between subjective neuropsychological assessments, measurable characteristics of the patient and the disease, and the type of treatment the patient had undergone one year before. We build upon results of previous analyses where have been found that the differences in neuropsychological assessment of the patients treated by either coiling or clipping was small and slightly in favor of surgical group. Using this data, we compare the “classical” statistical and data mining approach. While statistics offers techniques based on contingency tables, where the compared variables have to be manually selected, data mining methods like association rules, decision rules or decision trees offer the possibility to generate and evaluate a number of more complex hypotheses about the hidden relationships. We used SAS JMP to perform the statistical analysis and LISp-Miner system for the data mining experiments. © Springer International Publishing Switzerland 2015.