Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients' Asthma Hospital Visits to an Academic Healthcare System.
Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients' Asthma Hospital Visits to an Academic Healthcare System.
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DOI：
10.1109/access.2020.3032683
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Luo G
中科院分区：
文献类型：
--
作者：
Tong Y;Messinger AI;Luo G
哮喘给医疗保健带来了巨大的开销。为了实现有效的预防性护理，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Intermountain Healthcare数据，以预测哮喘患者在未来12个月内的哮喘医院就诊。正如机器学习中常见的那样，两种模型都没有为其预测提供解释。为了解决黑盒模型的可解释性问题，我们开发了一种自动化方法，为任何机器学习模型对不平衡表格数据的预测生成规则风格的解释，并在不降低预测准确性的情况下推荐定制的干预措施。我们的方法在解释我们的山间医疗保健模型的预测表现良好。然而，我们的方法推广到学术医疗系统的效果如何仍然未知，其患者组成与Intermountain Healthcare不同。本研究评估我们的自动化解释方法的一般性的学术保健系统华盛顿大学医学预测哮喘医院访问。我们对2011年至2018年期间华盛顿大学医学院的82，888例哮喘成年人数据进行了二次分析，使用我们的方法来解释我们的华盛顿大学医学院模型的预测并推荐定制干预措施。结果显示，我们的自动解释方法对于预测哮喘医院就诊有令人满意的推广性，华盛顿大学医学院。特别是，我们的方法解释了87.6%的哮喘患者的预测，我们的华盛顿大学医学模型准确预测了他们在未来12个月内会去医院就诊。
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.
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影响因子：
1.9
作者：
Sato, Ryuji;Tomita, Katsuyuki;Tohda, Yuji
通讯作者：
Tohda, Yuji
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：
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影响因子：
--
作者：
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通讯作者：
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影响因子：
16.6
作者：
Guidotti, Riccardo;Monreale, Anna;Pedreschi, Dino
通讯作者：
Pedreschi, Dino
影响因子：
14.2
作者：
Greineder, DK;Loane, KC;Parks, P
通讯作者：
Parks, P