Tong Yao, Messinger Amanda I, Luo Gang
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA.
Department of Pediatrics, Children's Hospital Colorado, The Breathing Institute, University of Colorado School of Medicine, Aurora, CO 80045, USA.
IEEE Access. 2020;8:195971-195979. doi: 10.1109/access.2020.3032683. Epub 2020 Oct 21.
Asthma puts a tremendous overhead on healthcare. To enable effective preventive care to improve outcomes in managing asthma, we recently created two machine learning models, one using University of Washington Medicine data and the other using Intermountain Healthcare data, to predict asthma hospital visits in the next 12 months in asthma patients. As is common in machine learning, neither model supplies explanations for its predictions. To tackle this interpretability issue of black-box models, we developed an automated method to produce rule-style explanations for any machine learning model's predictions made on imbalanced tabular data and to recommend customized interventions without lowering the prediction accuracy. Our method exhibited good performance in explaining our Intermountain Healthcare model's predictions. Yet, it stays unknown how well our method generalizes to academic healthcare systems, whose patient composition differs from that of Intermountain Healthcare. This study evaluates our automated explaining method's generalizability to the academic healthcare system University of Washington Medicine on predicting asthma hospital visits. We did a secondary analysis on 82,888 University of Washington Medicine data instances of asthmatic adults between 2011 and 2018, using our method to explain our University of Washington Medicine model's predictions and to recommend customized interventions. Our results showed that for predicting asthma hospital visits, our automated explaining method had satisfactory generalizability to University of Washington Medicine. In particular, our method explained the predictions for 87.6% of the asthma patients whom our University of Washington Medicine model accurately predicted to experience asthma hospital visits in the next 12 months.
哮喘给医疗保健带来了巨大负担。为了实现有效的预防保健以改善哮喘管理的结果，我们最近创建了两个机器学习模型，一个使用华盛顿大学医学数据，另一个使用山间医疗保健数据，来预测哮喘患者未来12个月内的哮喘住院情况。正如机器学习中常见的那样，这两个模型都没有为其预测提供解释。为了解决黑箱模型的可解释性问题，我们开发了一种自动化方法，可为任何基于不平衡表格数据做出预测的机器学习模型生成规则式解释，并在不降低预测准确性的情况下推荐定制化干预措施。我们的方法在解释山间医疗保健模型的预测方面表现良好。然而，我们的方法在学术医疗系统中的推广效果如何尚不清楚，因为学术医疗系统的患者构成与山间医疗保健不同。本研究评估了我们的自动化解释方法在预测哮喘住院情况时对华盛顿大学医学学术医疗系统的可推广性。我们对2011年至2018年间华盛顿大学医学的82888例成年哮喘患者数据实例进行了二次分析，使用我们的方法来解释华盛顿大学医学模型的预测并推荐定制化干预措施。我们的结果表明，对于预测哮喘住院情况，我们的自动化解释方法对华盛顿大学医学具有令人满意的可推广性。特别是，我们的方法解释了华盛顿大学医学模型准确预测在未来12个月内会经历哮喘住院的87.6%的哮喘患者的预测结果。
