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首页> 外文期刊>JMIR Medical Informatics >Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach
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Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

机译：联邦学习电子健康记录，以改善住院治疗患者的死亡率预测 - 19：机器学习方法

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Background Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.
机译：背景技术机器学习模型需要跨越不同的医疗机构静坐的大型数据集。专注于Covid-19的机器学习研究仅限于单医院数据，这限制了模型概括性。目的我们旨在使用联合学习，一种机器学习技术，避免跨多个机构局部临床数据，以在7天内预测住院治疗患者的死亡率。方法从山内卫生系统内5家医院的电子健康记录收集患者数据。通过在每个站点上使用本地数据训练具有L1正则化/最小绝对收缩和选择操作员（套索）和多层Perceptron（MLP）模型的Logistic回归。我们开发了具有来自所有5个站点的组合数据的汇总模型，以及仅使用中央聚合器共享参数的联合模型。结果Lassofederated Model在3家医院的Lassoferation模型中表现优于Lassoloc模式，并且MLPFederated模型比所有5个医院的MLPocal模型更好，由接收器操作特征曲线下的区域确定。 Lassopooled Model在所有医院的洛杉矶替代型模型表现优势，MLPFederated模型在2家医院的MLPPooled模型中表现优势。结论CoVID-19电子健康记录数据的联邦学习在不影响患者隐私的情况下开发强大的预测模型的承诺。

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