University of Pittsburgh

Transfer learning for Bayesian case detection systems

PhD student
Date: 
Friday, November 30, 2018 - 1:00pm - 1:30pm

In this age of big biomedical data, massive amounts of data have been produced worldwide. If we could effectively share all the information accumulated from all existing resources, we may develop a deeper understanding of biomedical problems and find better solutions.

Compared to traditional machine learning techniques, transfer learning techniques fully consider differences between shared parties in order to provide a smooth transfer of knowledge from source party to target party. Most well-established techniques focus on sharing data, while recent techniques have begun to explore the possibility of sharing models. Model-sharing techniques are especially appealing for biomedical area because of much less privacy risks. Unfortunately, most model-transferring techniques are unable to handle heterogeneous scenarios where feature spaces, marginal and conditional distributions differ among shared parties, which commonly exist in biomedical data.

My dissertation developed an innovative transfer learning framework to share data or model under heterogeneous scenarios. Heuristic scores have been designed to integrate source information with target data, while allowing injections of target-specific features for a better localization. Both synthetic and real-world datasets were used to test two hypotheses: 1) Transfer learning is better than using the model constructed with target data only; 2) Transfer learning is better than direct adoption of the source model. A comprehensive analysis was conducted to investigate conditions where these two hypotheses hold, and more generally the factors that affect the effectiveness of transfer learning, providing empirical opinions about when and what to share.

My research contributes to the fields of machine learning, medical informatics and disease surveillance. It enables knowledge sharing under heterogeneous scenarios and provides methodologies for diagnosing transfer learning performance under tasks varying degrees of feature space overlapping, similarities of distributions, and sample sizes. The model-transferring algorithm can be viewed as a new Bayesian network learning algorithm with a flexible representation of prior knowledge allowing partial feature coverage. To the best of my knowledge, this is the first exploration on model-transferring for biomedical data in heterogeneous scenarios. My work shows the potential of quick development of a case detection system for an emergent unknown disease and demonstrates its transferability and adaptability. 

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