Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis.
Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis.
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DOI：
10.2196/21965
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发表时间：
2020-12-31
影响因子：
3.2
通讯作者：
Stone BL
中科院分区：
文献类型：
--
作者：
Luo G;Johnson MD;Nkoy FL;He S;Stone BL
哮喘是一种主要的慢性疾病，给医疗保健带来沉重的负担。为了促进护理管理资源的分配，以改善高危哮喘患者的预后，我们最近建立了一个机器学习模型来预测哮喘患者在随后一年的哮喘医院就诊。我们的模型比以前的模型更准确。然而，与大多数机器学习模型一样，它没有解释其预测结果。这为护理管理中的使用创造了障碍，其中需要可解释性。本研究旨在开发一种方法来自动解释模型的预测结果，并在不降低模型性能指标的情况下推荐量身定制的干预措施。我们的数据是不平衡的，只有一小部分数据实例与未来的哮喘医院就诊有关。为了处理不平衡的数据，我们扩展了以前的方法，即自动为任何机器学习模型对表格数据的预测结果提供规则形式的解释，而不会降低模型的性能指标。在对Intermountain Healthcare在2005年至2018年期间用于形成我们模型的334，564个数据实例的二次分析中，我们采用了扩展方法来自动解释我们模型的预测结果并推荐量身定制的干预措施。患者队列包括2005年至2018年期间在Intermountain Healthcare接受治疗的所有哮喘患者，并根据访视时的记录居住在犹他州或爱达荷州。我们的方法解释了89.7%（391/436）的哮喘患者的预测结果，根据我们的模型的正确预测，这些患者可能在随后的一年中发生哮喘医院就诊。这项研究首次证明了自动为任何机器学习模型对不平衡表格数据的预测结果提供规则形式的解释的可行性，而不会降低模型的性能指标。经过进一步的改进，我们的哮喘预后预测模型加上自动解释功能，可用于指导临床医生分配有限的哮喘护理管理资源和识别适当的干预措施。
Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.
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影响因子：
2.5
作者：
Kern, Lisa M.;Grinspan, Zachary;Kaushal, Rainu
通讯作者：
Kaushal, Rainu
影响因子：
--
作者：
Bourgeois, Fabienne C.;Olson, Karen L.;Mandl, Kenneth D.
通讯作者：
Mandl, Kenneth D.
影响因子：
3.5
作者：
Hatwell J;Gaber MM;Atif Azad RM
通讯作者：
Atif Azad RM
DOI：
10.1164/ajrccm.157.4.9708124
发表时间：
1998-04-01
影响因子：
24.7
作者：
Lieu, TA;Quesenberry, CP;Leong, AB
通讯作者：
Leong, AB
影响因子：
--
作者：
Luo, Gang
通讯作者：
Luo, Gang