Transfer learning to improve the re-usability of computable biomedical knowledge - Candidate: With my multidisciplinary background in Artificial Intelligence (PhD), Public Health Informatics
(MS), Epidemiology and Health Statistics (MS), and Preventive Medicine (Bachelor of Medicine), my career
goal is to become an independent investigator working at the intersection of Artificial Intelligence and
Biomedicine, with a particular emphasis initially in machine learning and public health.
Training plan: My K99/R00 training plan emphasizes machine learning, deep learning and
scientific communication skills (presentation, writing articles, and grant applications), which will complement
my current strengths in artificial intelligence, statistics, medicine and public health. I have a very strong
mentoring team. My mentors, Drs. Michael Becich (primary), Gregory Cooper, Heng Huang, and Michael
Wagner, all of whom are experienced with research and professional career development.
Research plan: The research goal of my proposed K99/R00 grant is to increase the re-use of
computable biomedical knowledge, which is knowledge represented in computer-interpretable formalisms
such as Bayesian networks and neural networks. I refer to such representations as models. Although models
can be re-used in toto in another setting, there may be loss of performance or, even more
problematically, fundamental mismatches between the data required by the model and the data available in
the new setting making their re-use impossible. The field of transfer learning develops algorithms for
transferring knowledge from one setting to another. Transfer learning, a sub-area of machine learning,
explicitly distinguishes between a source setting, which has the model that we would like to re-use, and a
target setting, which has data insufficient for deriving a model from data and therefore needs to re-use a model
from a source setting. I propose to develop and evaluate several Bayesian Network Transfer Learning (BN-
TL) algorithms and a Convolutional Neural Network Transfer Learning algorithm. My specific research aims
are to: (1) further develop and evaluate BN-TL for sharing computable knowledge across healthcare
settings; (2) develop and evaluate BN-TL for updating computable knowledge over time; and (3) develop and
evaluate a deep transfer learning algorithm that combines knowledge in heterogeneous scenarios. I will do
this research on models that are used to automatically detect cases of infectious disease such as influenza.
Impact: The proposed research takes advantage of large datasets that I previously developed; therefore I
expect to quickly have results with immediate implications for how case detection models are shared from a
region that is initially experiencing an epidemic to another location that wishes to have optimal case-detection
capability as early as possible. More generally, it will bring insight into machine learning enhanced
biomedical knowledge sharing and updating. This training grant will prepare me to work independently and
lead efforts to develop computational solutions to meet biomedical needs in future R01 projects.
