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.
Highlights
As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling
The areas under the curves (AUCs) of 0.660, sensitivity of 62.5%, specificity of 69.6%, positive predictive value (PPV) of 6.0% and negative predictive value (NPV) of 98.4 were calculated at the optimal cut-off value
The initial data mining from the medical records included a review of fall risk-increasing drugs (FRIDs), e.g., cardiovascular drugs, antidiabetic drugs, and CNS drugs, and the results showed that cardiovascular drugs were associated with a relatively higher risk for fall events
Summary
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. 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. Other machine learning approaches have been attempted for identifying patients at risk of falling; a comparative validation with current fall risk assessment scales has never been involved[7]. This study aims to propose a machine learning model for fall risk assessment in competition with the MFS.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
