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Abstract Background Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors. Methods Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference. Results In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition. Conclusion We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.

This paper describes two different systems which can compensate harmonic currents generated in a power system. As non-linear loads increase gradually in industry fields, harmonic current generated in the electric power network system also increase. Harmonic currents make power network currents distorted and generate heat, vibration and noise in the power machinery. Many approaches have been applied to compensate harmonic currents generated in the power system. Among various approaches, in this paper, two approaches are compared and evaluated. They are flywheel compensators based on wounded rotor induction motor and squirrel cage induction motor. Both systems have a common structure. They use a flywheel as an energy storage device and use pulse width modulation inverters. The main differences are the size and rating of the converter used. [Copyright &y& Elsevier]