Application of Machine Learning Methods in Nursing Home Research

Abstract

Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…For the topic of falls, we identified 24 studies that met inclusion criteria. Of these studies, eight used a retrospective cohort design 40 41 42 43 44 45 46 47 ; seven used a prospective cohort design 48 49 50 51 52 53 54 ; six were secondary analyses of research data obtained from prospective, retrospective, and cross-sectional studies 55 56 57 58 59 60 ; one used mixed methods wherein data from a public dataset were used in conjunction with measurements collected from sensors 61 ; and one was a meta-analysis of prospective cohort and observational studies. 62 Ten of the studies used health records as a source of data but in two of these studies, 44 47 it was not clear whether the records were electronic when they were obtained.…”

“…Diagnoses and/or symptoms of the participants were tested as predictors in most of the studies. 40 41 42 43 44 45 46 47 49 50 51 52 54 56 57 58 59 60 62 63 Several categories of predictors were noteworthy, including strength, balance, and gait test scores 40 46 47 48 49 50 51 52 53 54 55 56 57 59 60 61 62 63 and nutritional status. 42 56 59 In 15 studies, prediction models were developed and evaluated with regression models.…”

Defining the Concepts of a Smart Nursing Home and Its Potential Technology Utilities That Integrate Medical Services and Are Acceptable to Stakeholders: a Scoping Review

“…Monitoring and noti cation of abnormal events: Monitoring devices have been proven to ensure the safety of the nursing home residents in fall prevention (32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), automatic monitoring of health conditions, and noti cation of emerging events such as heart attacks and fatal accidents (11,12,19,31,. The vital sign of older adults could be collected and recorded by the wearable devices such as clothes and shoes on nursing home residents (35, 95).…”

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

“…Recently, ML started gaining attention, and researchers from health, medicine, and nursing fields utilize ML because of those advantages. For example, ML has been used to investigate mortality predictors [ 15 ], disease prognosis and prediction [ 17 , 18 , 19 ], emergency department triage prediction [ 20 ], and fall prediction [ 21 ]. In addition, previous studies using ML concluded that ML showed a superior performance regarding hospital-related outcomes than traditional statistical approaches [ 20 , 21 ].…”

“…For example, ML has been used to investigate mortality predictors [15], disease prognosis and prediction [17][18][19], emergency department triage prediction [20], and fall prediction [21]. In addition, previous studies using ML concluded that ML showed a superior performance regarding hospitalrelated outcomes than traditional statistical approaches [20,21]. Therefore, ML may enhance the understanding of PU predictors among healthcare providers in long-term-care settings.…”