Zhou Zhou, Wang Danhui, Sun Jun, Zhu Min, Teng Liping
Author Affiliations: Wuxi School of Medicine, Jiangnan University, Jiangsu (Mr Zhou; Mss Wang, Sun, and Zhu; and Dr Teng); Traditional Chinese Medicine Hospital of Qinghai Province, Xining, Qinghai (Ms Wang), China.
Comput Inform Nurs. 2024 Dec 1;42(12):913-921. doi: 10.1097/CIN.0000000000001202.
Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model.
跌倒在老年人中是一种常见的不良事件。本研究旨在确定跌倒的关键因素，并开发一种基于机器学习的预测模型，以预测社区居住老年人的跌倒风险类别，从而实现早期干预并取得更好的结果。构建并评估了三种预测模型（逻辑回归、随机森林和朴素贝叶斯）。共有459人参与，其中156名参与者（34.0%）有高跌倒风险。选择了七个独立预测因素（虚弱状态、年龄、吸烟、心脏病发作、脑血管疾病、关节炎和骨质疏松症）来构建模型。在这三种机器学习模型中，逻辑回归模型具有最佳的模型拟合度，在测试集中曲线下面积最高（0.856），准确率（0.797）和灵敏度（0.735）也最高。逻辑回归模型具有出色的区分度、校准度和临床决策能力，有助于准确识别高危人群并借助该模型进行早期干预。
