Publication details

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

Authors

ŠÍN Petr HOKYNKOVÁ Alica NOVÁKOVÁ Marie POKORNÁ Andrea PODROUŽEK Jan KRČ Rostislav

Year of publication 2022
Type Article in Periodical
Magazine / Source MDPI Diagnostics
MU Faculty or unit

Faculty of Medicine

Citation
Web https://doi.org/10.3390/diagnostics12040850
Keywords pressure ulcer; pressure injury; machine learning; MIMIC database; MIMIC-IV; open data; artificial neural network; random forest
Description 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.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info