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@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 and Jan PODROUŽEK. Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data. \textit{Diagnostics}. Basel: MDPI, 2022, vol.~12, No~4, p.~1-13. ISSN~2075-4418. Available from: https://dx.doi.org/10.3390/diagnostics12040850.
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