TY -的盟Tarekegn Adane盟——Ricceri,非盟-科斯塔朱盟——Ferracin Elisa盟——Giacobini马里奥PY - 2020 DA - 2020/6/4 TI -老年人脆弱状况的预测建模:机器学习方法乔-地中海JMIR通知SP - e16678六世- 8 - 6千瓦预测建模KW -脆弱KW -机器学习KW -遗传规划KW -不平衡数据集KW -老年人KW -分类AB -背景:虚弱是老年人中最严重的与年龄相关的疾病之一。它通常被认为是晚年生理衰退的一种综合征，其特征是对不良健康结果的明显脆弱性。然而，到目前为止，对脆弱的一个明确的操作性定义还没有达成一致。关于虚弱的检测及其与死亡率的关系有广泛的研究。其中一些研究关注的是与老年人口虚弱相关的可能风险因素，而在临床环境中预测谁将面临更高的虚弱风险仍然被忽视。目的:我们的研究目的是基于临床特征和社会经济因素的数据库，使用不同的机器学习方法开发老年人虚弱状况的预测模型。方法:对1095,612名65岁及以上老年人的行政卫生数据库，采用58个输入变量和6个输出变量。我们首先确定并定义6个问题/输出作为脆弱的代表。然后，我们通过重采样过程解决了数据的不平衡性，并对不同的机器学习(ML)算法——人工神经网络(ANN)、遗传规划(GP)、支持向量机(SVM)、随机森林(RF)、逻辑回归(LR)和决策树(DT)——进行了比较研究。 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. SN - 2291-9694 UR - http://medinform.www.mybigtv.com/2020/6/e16678/ UR - https://doi.org/10.2196/16678 UR - http://www.ncbi.nlm.nih.gov/pubmed/32442149 DO - 10.2196/16678 ID - info:doi/10.2196/16678 ER -