Machine Learning-Based Pressure Ulcer Prediction in Modular
Critical Care Data

J 2022

Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data

ŠÍN, Petr, Alica HOKYNKOVÁ, Marie NOVÁKOVÁ, Andrea POKORNÁ, Rostislav KRČ et. al.

Základní údaje

Originální název

Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data

Autoři

ŠÍN, Petr (203 Česká republika, domácí), Alica HOKYNKOVÁ (703 Slovensko, garant, domácí), Marie NOVÁKOVÁ (203 Česká republika, domácí), Andrea POKORNÁ (203 Česká republika, domácí), Rostislav KRČ (203 Česká republika) a Jan PODROUŽEK (203 Česká republika)

Vydání

Diagnostics, Basel, MDPI, 2022, 2075-4418

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30218 General and internal medicine

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.600

Kód RIV

RIV/00216224:14110/22:00129663

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.3390/diagnostics12040850

UT WoS

000785475800001

Klíčová slova anglicky

pressure ulcer; pressure injury; machine learning; MIMIC database; MIMIC-IV; open data; artificial neural network; random forest

Štítky

14110229, 14110515, 14110611, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 20. 1. 2023 13:58, Mgr. Tereza Miškechová

Anotace

V originále

Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.

Návaznosti

NU21-09-00541, projekt VaV

Název: Role oxidativního stresu při hojení dekubitů u pacientů s míšní lézí

Investor: Ministerstvo zdravotnictví ČR, Role oxidativního stresu při hojení dekubitů u pacientů s míšní lézí, Podprogram 1 - standardní

Citovat

ŠÍN, Petr, Alica HOKYNKOVÁ, Marie NOVÁKOVÁ, Andrea POKORNÁ, Rostislav KRČ a Jan PODROUŽEK. Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data. Diagnostics. Basel: MDPI, 2022, roč. 12, č. 4, s. 1-13. ISSN 2075-4418. Dostupné z: https://dx.doi.org/10.3390/diagnostics12040850.

@article{1845958,
   author = {Šín, Petr and Hokynková, Alica and Nováková, Marie and Pokorná, Andrea and Krč, Rostislav and Podroužek, Jan},
   article_location = {Basel},
   article_number = {4},
   doi = {http://dx.doi.org/10.3390/diagnostics12040850},
   keywords = {pressure ulcer; pressure injury; machine learning; MIMIC database; MIMIC-IV; open data; artificial neural network; random forest},
   language = {eng},
   issn = {2075-4418},
   journal = {Diagnostics},
   title = {Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data},
   url = {https://doi.org/10.3390/diagnostics12040850},
   volume = {12},
   year = {2022}
}

TY  - JOUR
ID  - 1845958
AU  - Šín, Petr - Hokynková, Alica - Nováková, Marie - Pokorná, Andrea - Krč, Rostislav - Podroužek, Jan
PY  - 2022
TI  - Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data
JF  - Diagnostics
VL  - 12
IS  - 4
SP  - 1-13
EP  - 1-13
PB  - MDPI
SN  - 20754418
KW  - pressure ulcer
KW  - pressure injury
KW  - machine learning
KW  - MIMIC database
KW  - MIMIC-IV
KW  - open data
KW  - artificial neural network
KW  - random forest
UR  - https://doi.org/10.3390/diagnostics12040850
N2  - Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.
ER  -

ŠÍN, Petr, Alica HOKYNKOVÁ, Marie NOVÁKOVÁ, Andrea POKORNÁ, Rostislav KRČ a Jan PODROUŽEK. Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data. \textit{Diagnostics}. Basel: MDPI, 2022, roč.~12, č.~4, s.~1-13. ISSN~2075-4418. Dostupné z: https://dx.doi.org/10.3390/diagnostics12040850.

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