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ŠÍN, P. HOKYNKOVÁ, A. NOVÁKOVÁ, M. POKORNÁ, A. KRČ, R. PODROUŽEK, J.
Original Title
Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data
Type
journal article in Web of Science
Language
English
Original Abstract
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.
Keywords
pressure ulcer; pressure injury; machine learning; MIMIC database; MIMIC-IV; open data; artificial neural network; random forest
Authors
ŠÍN, P.; HOKYNKOVÁ, A.; NOVÁKOVÁ, M.; POKORNÁ, A.; KRČ, R.; PODROUŽEK, J.
Released
30. 3. 2022
Publisher
MDPI
Location
Basel, Switzerland
ISBN
2075-4418
Periodical
Diagnostics
Year of study
12
Number
4
State
Swiss Confederation
Pages from
1
Pages to
13
Pages count
URL
https://www.mdpi.com/2075-4418/12/4/850/pdf
BibTex
@article{BUT179438, author="ŠÍN, P. and HOKYNKOVÁ, A. and NOVÁKOVÁ, M. and POKORNÁ, A. and KRČ, R. and PODROUŽEK, J.", title="Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data", journal="Diagnostics", year="2022", volume="12", number="4", pages="1--13", doi="10.3390/diagnostics12040850", issn="2075-4418", url="https://www.mdpi.com/2075-4418/12/4/850/pdf" }