Methodological Overview of Prospensity Score Matching Methods demonstrated on Colorectal Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252879" target="_blank" >RIV/61989100:27240/23:10252879 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10194437" target="_blank" >https://ieeexplore.ieee.org/document/10194437</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IDT59031.2023.10194437" target="_blank" >10.1109/IDT59031.2023.10194437</a>

Result language
angličtina
Original language name
Methodological Overview of Prospensity Score Matching Methods demonstrated on Colorectal Data
Original language description
Propensity score matching (PSM) is a method which is recommended to use at the case that data sets are not comparable. There exists several approaches at the definition of propensity score. We focused on two of them: model of logistic regression and machine learning method boosting. Propensity score principle can be applied in two ways: propensity score matching and weighting methods. The purpose of this study is to compare different PSM approaches and their influence on decision-making about the differences between two groups of data. Theoretical approach was demonstrated on biomedical data from the resort surgery.
Czech name
—
Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Conference on Information and Digital Technologies 2023, IDT 2023
ISBN
979-8-3503-0587-6
ISSN
2575-6753
e-ISSN
2575-677X
Number of pages
8
Pages from-to
89-96
Publisher name
IEEE
Place of publication
Piscataway
Event location
Žilina
Event date
Jun 20, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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