Open AccessPredictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms
Author(s) -
Jiyoun Song,
Kyungmi Woo,
Jingjing Shang,
Marietta Ojo,
Maxim Topaz
Publication year - 2021
Publication title -
advances in skin and wound care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.426
H-Index - 61
eISSN - 1538-8654
pISSN - 1527-7941
DOI - 10.1097/01.asw.0000755928.30524.22
Subject(s) - medicine , logistic regression , stepwise regression , bivariate analysis , odds ratio , machine learning , odds , random forest , emergency medicine , artificial intelligence , intensive care medicine , algorithm , computer science
Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate description of a patient's condition by extracting risk factors from clinical notes to build predictive models to identify a patient's risk of wound infection in HHC.