Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study.
Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study.
复制标题
DOI：
10.2196/28287
复制
发表时间：
2021-08-11
影响因子：
3.2
通讯作者：
Luo G
中科院分区：
文献类型：
--
作者：
Zhang X;Luo G
哮喘病医院的遭遇给医疗系统带来了沉重的负担。为了改善哮喘患者的预防性护理和结果，我们最近开发了一个黑盒机器学习模型来预测哮喘患者在接下来的12个月内是否会有一次或多次哮喘医院就诊。我们的模型比以前的模型更准确。然而，黑盒机器学习模型并不能解释它们的预测，这对广泛的临床应用构成了障碍。为了解决这个问题，我们之前开发了一种方法，可以自动为模型的预测提供基于规则的解释，并在不牺牲模型性能的情况下建议量身定制的干预措施。对于我们的模型正确预测的未来哮喘医院就诊的平均患者，我们的解释方法生成了超过5000个基于规则的解释（如果有的话）。然而，自动解释功能的用户，通常是忙碌的临床医生，将希望通过仅查看前几个解释来快速获得对患者最有用的信息。因此，需要一种方法来适当地对为患者生成的解释进行排序。然而，这目前是一个公开的问题。本研究的目的是开发一种方法，适当地排名规则为基础的解释，我们的自动化解释方法生成的病人。我们开发了一种在多个因素之间取得平衡的排名方法。通过对2011年至2018年期间来自华盛顿大学医学院的82，888例成人哮喘患者数据实例的二次分析，我们展示了预测哮喘患者哮喘医院就诊的测试案例的排名方法。对于每个预测在接下来的12个月内有哮喘医院就诊的患者，我们的排名方法返回的前几个解释通常具有高质量和低冗余。许多排名靠前的解释提供了关于患者情况的各个方面的有用见解，这些见解不能通过查看当前电子健康记录系统中的患者数据来轻松获得。解释排序模块是自动解释功能的重要组成部分，它解决了阻碍机器学习预测模型在临床实践中广泛采用的可解释性问题。在接下来的几年里，我们计划在解决其他疾病的预测建模问题以及来自其他医疗保健系统的数据上测试我们的解释排名方法。RR2-10.2196/5039
Asthma hospital encounters impose a heavy burden on the health care system. To improve preventive care and outcomes for patients with asthma, we recently developed a black-box machine learning model to predict whether a patient with asthma will have one or more asthma hospital encounters in the succeeding 12 months. Our model is more accurate than previous models. However, black-box machine learning models do not explain their predictions, which forms a barrier to widespread clinical adoption. To solve this issue, we previously developed a method to automatically provide rule-based explanations for the model’s predictions and to suggest tailored interventions without sacrificing model performance. For an average patient correctly predicted by our model to have future asthma hospital encounters, our explanation method generated over 5000 rule-based explanations, if any. However, the user of the automated explanation function, often a busy clinician, will want to quickly obtain the most useful information for a patient by viewing only the top few explanations. Therefore, a methodology is required to appropriately rank the explanations generated for a patient. However, this is currently an open problem. The aim of this study is to develop a method to appropriately rank the rule-based explanations that our automated explanation method generates for a patient. We developed a ranking method that struck a balance among multiple factors. Through a secondary analysis of 82,888 data instances of adults with asthma from the University of Washington Medicine between 2011 and 2018, we demonstrated our ranking method on the test case of predicting asthma hospital encounters in patients with asthma. For each patient predicted to have asthma hospital encounters in the succeeding 12 months, the top few explanations returned by our ranking method typically have high quality and low redundancy. Many top-ranked explanations provide useful insights on the various aspects of the patient’s situation, which cannot be easily obtained by viewing the patient’s data in the current electronic health record system. The explanation ranking module is an essential component of the automated explanation function, and it addresses the interpretability issue that deters the widespread adoption of machine learning predictive models in clinical practice. In the next few years, we plan to test our explanation ranking method on predictive modeling problems addressing other diseases as well as on data from other health care systems. RR2-10.2196/5039
登录
查看更多内容
影响因子：
12
作者：
Altaf, Wasif;Shahbaz, Muhammad;Guergachi, Aziz
通讯作者：
Guergachi, Aziz
影响因子：
3.2
作者：
Luo G;Johnson MD;Nkoy FL;He S;Stone BL
通讯作者：
Stone BL
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
影响因子：
8.5
作者：
Chen, Mu-Chen
通讯作者：
Chen, Mu-Chen