@文章{信息:doi/10.2196/16678，作者="Tarekegn, Adane和Ricceri, Fulvio和Costa, Giuseppe和Ferracin, Elisa和Giacobini, Mario"，标题="老年人虚弱状况的预测建模:机器学习方法"，期刊="JMIR Med Inform"，年="2020"，月="Jun"，日="4"，卷="8"，数="6"，页="e16678"，关键词="预测建模;脆弱;机器学习;遗传规划;不平衡数据集;老年人;背景:虚弱是老年人中最重要的年龄相关疾病之一。它通常被认为是晚年生理机能下降的一种综合征，其特征是对不良健康结果的明显脆弱性。然而，到目前为止，尚未就脆弱性的明确操作定义达成一致。有广泛的研究检测虚弱及其与死亡率的关系。 Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings. Objective: The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors. Methods: An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms -- Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) -- was carried out. The performance of each model was evaluated using a separate unseen dataset. Results: Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics. Conclusions: We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults. ", issn="2291-9694", doi="10.2196/16678", url="http://medinform.www.mybigtv.com/2020/6/e16678/", url="https://doi.org/10.2196/16678", url="http://www.ncbi.nlm.nih.gov/pubmed/32442149" }