Detailed Information on Publication Record
2015
Analysis of Neurosurgery Data Using Statistical and Data Mining Methods
BERKA, Petr, J. JABLONSKÝ, Luboš MAREK and Michal VRABECBasic information
Original name
Analysis of Neurosurgery Data Using Statistical and Data Mining Methods
Authors
BERKA, Petr (203 Czech Republic, guarantor, belonging to the institution), J. JABLONSKÝ (203 Czech Republic), Luboš MAREK (203 Czech Republic) and Michal VRABEC (203 Czech Republic)
Edition
9414. vyd. Switzerland, Advances in Artificial Intelligence and Its Applications. 14th. Mexican Int. Conference on Artificial Intelligence, p. 310-321, 12 pp. 2015
Publisher
Springer International Publishing
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Switzerland
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
RIV identification code
RIV/04274644:_____/15:#0000087
Organization unit
University of Finance and Administration
ISBN
978-3-319-27100-2
ISSN
UT WoS
000367681400023
Keywords in English
Engineering controlled terms; Artificial intelligence; Decision trees; Neurosurgery; Patient monitoring; Patient treatment; Surgery; Trees (mathematics)
Tags
International impact, Reviewed
Změněno: 29/3/2017 14:52, Ing. Dominika Moravcová
Abstract
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.