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
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
Klíčová slova anglicky
Engineering controlled terms; Artificial intelligence; Decision trees; Neurosurgery; Patient monitoring; Patient treatment; Surgery; Trees (mathematics)
Příznaky
Mezinárodní význam, Recenzováno
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.
Zobrazeno: 31. 10. 2024 14:14