接收机工作特性
计算机科学
背景(考古学)
人工智能
机器学习
灵活性(工程)
召回
预测建模
任务(项目管理)
F1得分
预测分析
数据挖掘
统计
数学
心理学
认知心理学
古生物学
管理
经济
生物
作者
Luis Mendoza-Pittí,José Manuel Gómez-Pulido,Miguel Vargas-Lombardo,Juan A. Gómez-Pulido,María-Luz Polo-Luque,Diego Rodríguez-Puyol
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 72065-72079
标识
DOI:10.1109/access.2022.3189018
摘要
Predicting whether patients will experience intradialytic hypotension (IDH) during hemodialysis (HD) is not an easy task. IDH is associated with multiple risk factors, meaning that traditional statistical models are unable to find the relationships that affect it. In this context, the use of models based on machine learning (ML) can allow the discovery of complex relationships, since they can solve problems without being explicitly programmed. In this work we developed, evaluated and identified an ML-based model that is capable of predicting at the beginning of the HD session whether a patient will suffer from IDH during its prolonged development. To develop the ML models, we used the hold-out and cross-validation methods; while, to evaluate the performance of the models we used the metrics F1-score, Matthews Correlation Coefficient, areas under the receiver operating characteristic (AUROC) and precision-recall curve (AUPRC). In this sense, we selected and used a reduced combination of variables from clinical records and blood analytics, which have proven to be decisive for the occurrence of IDH. The predictive results obtained through our work confirmed that the best ML model was based on the XGBoost model, achieving values of 0.969 and 0.945 for AUROC and AUPRC respectively. Therefore, our study suggests that the XGBoost model has a very high predictive capacity for the appearance of an IDH in HD patients and presents great versatility and flexibility in terms of supporting informed decision-making by medical staff.
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