Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)

Authors

  • Qizhen Zhang McGill University
  • Audrey Durand Universit√© Laval
  • Joelle Pineau McGill University

DOI:

https://doi.org/10.1609/aaai.v34i10.7264

Abstract

Applications of machine learning in biomedical prediction tasks are often limited by datasets that are unrepresentative of the sampling population. In these situations, we can no longer rely only on the the training data to learn the relations between features and the prediction outcome. Our method proposes to learn an inductive bias that indicates the relevance of each feature to outcomes through literature mining in PubMed, a centralized source of biomedical documents. The inductive bias acts as a source of prior knowledge from experts, which we leverage by imposing an extra penalty for model weights that differ from this inductive bias. We empirically evaluate our method on a medical prediction task and highlight the importance of incorporating expert knowledge that can capture relations not present in the training data.

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Published

2020-04-03

How to Cite

Zhang, Q., Durand, A., & Pineau, J. (2020). Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13983-13984. https://doi.org/10.1609/aaai.v34i10.7264

Issue

Section

Student Abstract Track