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首页> 外文期刊>JMIR Medical Informatics >A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support
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A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support

机译：一种自动化谱系追踪的路线图，以帮助自动解释临床决策支持的机器学习预测

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Using machine learning predictive models for clinical decision support has great potential in improving patient outcomes and reducing health care costs. However, most machine learning models are black boxes that do not explain their predictions, thereby forming a barrier to clinical adoption. To overcome this barrier, an automated method was recently developed to provide rule-style explanations of any machine learning model’s predictions on tabular data and to suggest customized interventions. Each explanation delineates the association between a feature value pattern and an outcome value. Although the association and intervention information is useful, the user of the automated explaining function often requires more detailed information to better understand the patient’s situation and to aid in decision making. More specifically, consider a feature value in the explanation that is computed by an aggregation function on the raw data, such as the number of emergency department visits related to asthma that the patient had in the prior 12 months. The user often wants to rapidly drill through to see certain parts of the related raw data that produce the feature value. This task is frequently difficult and time-consuming because the few pieces of related raw data are submerged by many pieces of raw data of the patient that are unrelated to the feature value. To address this issue, this paper outlines an automated lineage tracing approach, which adds automated drill-through capability to the automated explaining function, and provides a roadmap for future research.
机译：利用机器学习预测模型，临床决策支持具有巨大潜力，从而提高患者结果并降低医疗费用。然而，大多数机器学习模型都是黑匣子，不解释他们的预测，从而形成临床采用的屏障。为了克服这一屏障，最近开发了一种自动化方法来提供任何机器学习模型对表格数据的预测的规则风格解释，并建议定制干预措施。每个解释都描绘了特征值模式和结果值之间的关联。尽管协会和干预信息是有用的，但是自动解释功能的用户通常需要更详细的信息来更好地了解患者的情况并帮助决策。更具体地，考虑通过对原始数据的聚合功能计算的解释中的特征值，例如与患者在之前的12个月内患有哮喘相关的急诊部门访问的数量。用户通常希望快速钻取以查看产生特征值的相关原始数据的某些部分。这项任务经常困难且耗时，因为少量相关的原始数据被患者与特征值无关的患者的许多原始数据淹没。为了解决这个问题，本文概述了自动化谱系追踪方法，它为自动化解释功能增加了自动呼吸能力，并为未来的研究提供了路线图。

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