机器学习方法预测初级保健安全网中更高的COVID-19护理负担:西班牙裔患者人口规模是一个关键因素。

IF 1.5 Q3 HEALTH POLICY & SERVICES
Health Services Research and Managerial Epidemiology Pub Date : 2022-08-02 eCollection Date: 2022-01-01 DOI:10.1177/23333928221115894
Evan V Goldstein, Fernando A Wilson
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引用次数: 0

摘要

导语:联邦政府立法提供补充资金,以支持社区卫生中心(CHCs)应对COVID-19大流行。补充资金包括标准基本付款和大流行前服务的总人数和无保险患者人数的调整。然而,并非所有CHCs都具有相似的患者群体特征和健康风险。目的:利用机器学习识别预测CHCs在大流行的第一年是否有高COVID-19患者负担的最重要因素。方法:我们的分析样本包括2020年50个州和华盛顿特区1342个CHCs的数据。我们训练了一个随机森林(RF)分类器模型,结合5倍交叉验证来验证RF模型,同时优化模型的超参数。最终的性能指标是在应用最适合hold out测试集的模型之后计算的。结果:2020年,高负担CHCs平均每1000例患者中有65.3例被诊断为COVID-19。我们的RF模型准确度为80.9%,精密度为80.1%,灵敏度为25.0%,特异性为98.1%。2020年西班牙裔患者的比例是预测CHCs是否有高COVID-19负担的最重要特征。结论:我们的RF模型结果表明,患者人群种族和民族特征对于预测2020年诊断为COVID-19的CHCs患者是否具有高负担最重要,尽管敏感性较低。加强对服务于大量西班牙裔患者群体的保健中心的支持可能会对应对未来的COVID-19浪潮产生影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.

Introduction: The federal government legislated supplemental funding to support community health centers (CHCs) in response to the COVID-19 pandemic. Supplemental funding included standard base payments and adjustments for the number of total and uninsured patients served before the pandemic. However, not all CHCs share similar patient population characteristics and health risks.

Objective: To use machine learning to identify the most important factors for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 during the first year of the pandemic.

Methods: Our analytic sample included data from 1342 CHCs across the 50 states and D.C. in 2020. We trained a random forest (RF) classifier model, incorporating 5-fold cross-validation to validate the RF model while optimizing the model's hyperparameters. Final performance metrics were calculated following the application of the model that had the best fit to the held-out test set.

Results: CHCs with a high burden of COVID-19 had an average of 65.3 patients diagnosed with COVID-19 per 1000 patients in 2020. Our RF model had 80.9% accuracy, 80.1% precision, 25.0% sensitivity, and 98.1% specificity. The percentage of Hispanic patients served in 2020 was the most important feature for predicting whether CHCs had high COVID-19 burden.

Conclusions: Findings from our RF model suggest patient population race and ethnicity characteristics were most important for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 in 2020, though sensitivity was low. Enhanced support for CHCs serving large Hispanic patient populations may have an impact on addressing future COVID-19 waves.

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来源期刊
CiteScore
1.60
自引率
6.20%
发文量
32
审稿时长
12 weeks
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