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Robust Interpretable Rule Learning to Identify Expertise Transfer Opportunities in Healthcare
Willa Potosnak · Sebastian Caldas Rivera · Gilles Clermont · Kyle Miller · Artur Dubrawski

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in Bridging the Gap: from Machine Learning Research to Clinical Practice »

Differences in clinical outcomes and costs within and between healthcare sites are a result of varying patient populations. We aim to pragmatically leverage this population heterogeneity and identify opportunities for beneficial transfer of knowledge across healthcare sites. We propose an algorithmic approach that is robust to sampling variance and yields reliable and human-interpretable insights into knowledge transfer opportunities. Our experimental results, obtained with two intensive care monitoring datasets, demonstrate the potential utility of the proposed method in clinical practice.

Author Information

Willa Potosnak (Carnegie Mellon University)
Sebastian Caldas Rivera (Carnegie Mellon University)
Gilles Clermont (University of Pittsburgh)
Kyle Miller (Carnegie Mellon University)
Artur Dubrawski (Carnegie Mellon University)

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