背景:虚弱是一种老年综合征,其特征是个体易损性增加,暴露于外部压力源时依赖性和死亡率均增加。衰弱指数在常规临床实践中的使用受到几个因素的限制,比如病人的认知状态,协商时间,或缺乏患者的先验信息。
目的:在本研究中,我们提出了一种客观的弱点衡量标准,基于来自手握力(HGS)的信号。
方法:使用改进的Deyard测功机记录该信号,并使用基于监督学习方法的机器学习策略进行处理,以训练分类器。在一项横向试点研究中,从138名老年人的队列中生成了一个数据库,该研究将经典的老年问卷调查与生理数据相结合。
方法:参与者是由合作实体提供医疗服务的老年病学家选择的患者。
结果:为了处理生成的信息,过滤了HGS数据集的20个选定的重要特征,清洁,并提取。基于从最小组生成新样本的合成少数过采样技术(SMOTE)和去除噪声样本的ENN(基于K-最近邻的技术)的组合的技术作为数据的良好平衡分布提供了最佳结果。
结论:训练随机森林分类器以92.9%的准确度预测脆弱标签,敏感度高于90%。
BACKGROUND: Frailty is a geriatric syndrome characterized by increased individual vulnerability with an increase in both dependence and mortality when exposed to external stressors. The use of Frailty Indices in routine clinical practice is limited by several factors, such as the cognitive status of the patient, times of consultation, or lack of prior information from the patient.
OBJECTIVE: In this study, we propose the generation of an objective measure of frailty, based on the signal from hand grip strength (HGS).
METHODS: This signal was recorded with a modified Deyard dynamometer and processed using machine learning strategies based on supervised learning methods to train classifiers. A database was generated from a cohort of 138 older adults in a transverse pilot study that combined classical geriatric questionnaires with physiological data.
METHODS: Participants were patients selected by geriatricians of medical services provided by collaborating entities.
RESULTS: To process the generated information 20 selected significant features of the HGS dataset were filtered, cleaned, and extracted. A technique based on a combination of the Synthetic Minority Oversampling Technique (SMOTE) to generate new samples from the smallest group and ENN (technique based on K-nearest neighbors) to remove noisy samples provided the best results as a well-balanced distribution of data.
CONCLUSIONS: A Random Forest Classifier was trained to predict the frailty label with 92.9% of accuracy, achieving sensitivities higher than 90%.