F. Kuusisto, I. Dutra, M. Elezaby, E. Mendonca, J. Shavlik & E. Burnside (2015).
Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems. Proceedings of the AMIA Joint Summits on Translational Science.
Slides (PDF).

This publication is contained in the following 2 PDF files
File 1, File 2.

Abstract:

While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p =0.004) without missing a single malignancy.

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