Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods

Abstract

Background: Machine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions. Objective: The purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs). Methods: We applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60). Results: The random forest model showed the greatest accuracy (0.814), followed by logistic regression (0.782), polynomial SVM (0.779), radial SVM (0.770), linear SVM (0.767), and sigmoid SVM (0.674). Conclusions: The random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents’ characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.

Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study

“…The polynomial SVM is used to process imaging data, while the RBF SVM is used when there is no prior information about the data. Moreover, the sigmoid SVM is associated with neural networks ( Lee et al, 2021 ). Our results revealed that the RBF SVM and sigmoid SVM performed better than linear and polynomial SVM, which had relatively higher AUC and accuracy, indicating that these features were nonlinear.…”

Nursing Home Characteristics and Resident Quality of Care Outcomes: A Scoping Review

“…The primary facility-level characteristic studied is staffing, including staff satisfaction, hours per resident per day (HPRPD), skill mix and staff accreditation, areas theorised to influence QoC and resulting resident outcomes (Plaku-Alakbarova, Punnett & Gore, 2018;Zhang et al, 2022). Other research examines, with various degrees of support, the relationships between resident outcomes and payer ratio or payer mix (e.g., privately vs. publicly funded), physical and social environments, facility size, occupancy, and location (Lee et al, 2021;Winblad, Blomqvist & Karlsson, 2017;You et al, 2016). Similarly, resident outcomes of focus vary across the research.…”

“…Care quality can be perceived differently across stakeholders such as residents, families, regulators, payers (such as CMS in the US) staff, and the larger public, all of whom may have different definitions and priorities (Kusmaul & Tucker, 2020). While some research and policy focus on clinical resident outcomes (e.g., falls, hospitalisations) as indicators of care quality, others emphasise residents' and family members' reported satisfaction as key to understanding QoC (Lee et al, 2021;Schweighart et al, 2022). As a result, efforts to understand and improve care quality in nursing facilities are segmented and limited in scope.…”

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

“…The majority of studies were published between 2020 and the end of 2022. The countries in which the studies were conducted were diverse: six studies were conducted in the US [20][21][22][23][24][25], four in Australia [26][27][28][29], three in Japan [30][31][32] and China [33][34][35], two in Korea [36,37], France [38,39], Spain [40,41], one in the United Kingdom [42], the Netherlands [43], Ireland [44], Canada [45], and Belgium [46]. The number of included LTC facilities and the size of the study population varied greatly between publications.…”