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首页> 外文期刊>JMIR Medical Informatics >Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records
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Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

机译：改善成人血红蛋白预测：使用电子健康记录的机器学习算法

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Background Predicting the risk of glycated hemoglobin (HbA 1c ) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients can ultimately help provide better health outcomes. Objective Our study investigated the performance of predictive models to forecast HbA 1c elevation levels by employing several machine learning models. We also examined the use of patient electronic health record longitudinal data in the performance of the predictive models. Explainable methods were employed to interpret the decisions made by the black box models. Methods This study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (5.7%) and elevated (≥5.7%) levels of HbA 1c . We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large data set from Saudi Arabia with 18,844 unique patient records. Results The machine learning models achieved promising results for predicting current HbA 1c elevation risk. When coupled with longitudinal data, the machine learning models outperformed the multiple logistic regression model used in the comparative study. The multilayer perceptron model achieved an accuracy of 83.22% for the area under receiver operating characteristic curve when used with historical data. All models showed a close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. Conclusions This study shows that machine learning models can provide promising results for the task of predicting current HbA 1c levels (≥5.7% or less). Using patients’ longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies.
机译：背景技术预测糖化血红蛋白（HBA 1C）升高的风险可以帮助识别患者患有发育严重慢性健康问题的患者，例如糖尿病。基于使用电子健康的先进预测模型的早期预防性干预记录用于识别这些患者的数据最终可以帮助提供更好的健康结果。目的我们的研究调查了通过采用多种机器学习模型来预测HBA 1C高程水平的预测模型的性能。我们还检查了使用患者电子健康记录纵向数据的性能预测模型。可说明的方法是用于解释黑匣子模型所做的决定。方法本研究采用多元回归，随机森林，支持向量机和逻辑回归模型，以及深入学习模型（多层Perceptron），分类正常（＆5.7％）和升高（≥5.7％）水平的患者HBA 1C。我们还通过以前访问的历史（纵向）数据集成了当前访问数据。可说明的机器学习方法用于询问模型，并对模型所做的决策背后的原因提供了理解。所有型号均经过培训和测试，使用来自沙特阿拉伯的大型数据设置，具有18,844次独特的患者记录。结果机器学习模型实现了预测当前HBA 1C高程风险的有希望的结果。当耦合与纵向数据时，机器学习模型表现优于比较研究中使用的多个逻辑回归模型。当与历史数据一起使用时，多层Perceptron模型在接收器操作特性曲线下的区域实现了83.22％的准确性。所有型号均显示有关随机血糖和年龄变量的贡献达成近似的协议，而没有纵向数据。结论本研究表明，机器学习模型可以为预测当前HBA 1C水平（≥5.7％或更低）提供有前途的结果。使用患者的纵向数据提高了性能并影响了使用预测器的相对重要性。模型显示结果与可比研究一致。

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