用于哮喘患者相关住院就诊的机器学习预测结果的自动解释方法的可推广性：定量分析。
Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis.
机构信息
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.
Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
出版信息
J Med Internet Res. 2021 Apr 15;23(4):e24153. doi: 10.2196/24153.
BACKGROUND
Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown.
OBJECTIVE
The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits.
METHODS
Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018.
RESULTS
Our method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months.
CONCLUSIONS
For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.
背景
哮喘给患者和医疗保健系统带来了巨大负担。为了促进哮喘管理的预防保健并改善患者的预后，我们最近开发了两种机器学习模型，一种基于 Intermountain Healthcare 数据，另一种基于 Kaiser Permanente Southern California（KPSC）数据，用于预测在接下来的 12 个月中哮喘患者的哮喘相关住院就诊次数，包括急诊就诊和住院治疗。与机器学习方法一样，这两种模型都无法解释其预测结果。为了解决黑盒模型的可解释性问题，我们设计了一种自动方法，为任何基于不平衡表格数据的机器学习模型的预测结果提供规则格式的解释，并建议在不损失准确性的情况下进行定制干预。我们的方法很好地解释了我们的 Intermountain Healthcare 模型的预测结果，但它在其他医疗保健系统中的通用性尚不清楚。
目的
本研究的目的是评估我们的自动解释方法对 KPSC 预测哮喘相关住院就诊的通用性。
方法
通过对 KPSC 2012 年至 2017 年的 987506 个数据实例进行二次分析，我们使用我们的方法来解释我们的 KPSC 模型的预测结果，并提出定制的干预措施。该患者队列涵盖了在 2015 年至 2018 年期间任何时间段内拥有 KPSC 健康计划的随机抽取的 70%的哮喘患者。
结果
我们的方法解释了在接下来的 12 个月中被正确预测将进行哮喘相关住院就诊的 97.57%（2204/2259）的哮喘患者的预测结果。
结论
对于预测哮喘相关住院就诊，我们的自动解释方法对 KPSC 具有可接受的通用性。
国际注册报告标识符（IRRID）：RR2-10.2196/resprot.5039。
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