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.

Transfer learning to improve the re-usability of computable biomedical knowledge Narrative Re-using computable biomedical knowledge in the form of a mathematical model in a new setting is challenging because the new setting may not have data needed as inputs to the model. This project will develop and evaluate transfer learning algorithms, which are computer programs that adapt a model to a new setting by removing and adding local variables to it. The developed methods for re-using models are expected to benefit the public’s health by: (1) improving case detection during epidemics by enabling re-use of automatic case detectors developed in the earliest affected regions with other regions, and, more generally, (2) increasing the impact of NIH’s investment in machine learning by enabling machine-learned models to be used in more institutions and locations.