Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study

Abstract

As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling. This study proposes an automatic fall risk prediction model based on eXtreme gradient boosting (XGB), using a data-driven approach to the standardized medical records. This study analyzed a cohort of 639 participants (297 fall patients and 342 controls) from Chang Gung Memorial Hospital, Chiayi Branch, Taiwan. A derivation cohort of 507 participants (257 fall patients and 250 controls) was collected for constructing the prediction model using the XGB algorithm. A comparative validation of XGB and the Morse Fall Scale (MFS) was conducted with a prospective cohort of 132 participants (40 fall patients and 92 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. This machine learning method provided a higher sensitivity than the standard method for fall risk stratification. In addition, the most important predictors found (Department of Neuro-Rehabilitation, Department of Surgery, cardiovascular medication use, admission from the Emergency Department, and bed rest) provided new information on in-hospital fall event prediction and the identification of patients with a high fall risk.

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.…”

“…Several of the studies, including two of the secondary analyses, incorporated data from mobility and gait sensors. 48 49 51 53 55 60 61 Registries and administrative datasets were used in eight studies, 40 41 42 43 45 46 50 56 while questionnaires or surveys were a source of data for four studies. 49 51 57 60 With the exception of one study that employed sensor data from 17-year-old persons, 55 all study participants were community dwelling, inpatient, and outpatient adults.…”

“…In several studies, the fall was self-reported, 49 51 57 60 63 but if it occurred while the participant was in an inpatient setting or being tracked in an outpatient setting, it was often documented in medical records, registries, or administrative databases used to track adverse events. 40 41 42 43 44 45 46 47 48 50 52 56 These differences in measuring the outcome are important, in that predictive models may then be more or less accurate, merely because of the accuracy or inaccuracy of the outcome measurement. The types of predictors across studies were quite consistent, nevertheless.…”

“…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.…”

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

“…Ensemble machine learning methods such as gradient boosting iteratively combines a set of weak base classification models to construct a strong learner. Gradient boosting techniques are currently being employed to attain state-of-the-art results in clinical applications [45,46]. Gradient boosting techniques sequentially minimize the residual error of preceding learners.…”