JMIR Publications

ABSTRACT

Using machine learning predictive models for clinical decision support has great potential to improve patient outcomes and reduce healthcare costs. However, most machine learning models are black boxes that do not explain their predictions, forming a barrier to clinical adoption. To overcome this barrier, we recently developed an automated method 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 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 unrelated to the feature value. To address this issue, this paper outlines an automated lineage tracing approach that adds automated drill-through capability to the automated explaining function, providing a roadmap for future research.