Transfer learning to improve the re-usability of computable biomedical knowledge

迁移学习提高可计算生物医学知识的可重用性

• 批准号: 10597207
• 负责人: Ye Ye
• 金额: $ 23.66万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10597207
• 关键词: Affect; Algorithms; Applications Grants; Area; Artificial Intelligence; Bayesian Method; Bayesian Modeling; Bayesian Network; Big Data; Clinical; Communicable Diseases; Communication; Complement; Computerized Medical Record; Computers; Data; Detection; Development; Diagnosis; Disease; Epidemic; Epidemiology; Future; Goals; Grant; Health; Healthcare Systems; Heterogeneity; Influenza; Institution; Investigation; Investments; Knowledge; Lead; Learning; Location; Machine Learning; Medical center; Medicine; Mentors; Methods; Modeling; Natural Language Processing; Parainfluenza; Patients; Performance; Play; Preventive Medicine; Process; Public Health; Public Health Informatics; Research; Research Personnel; Respiratory Disease; Role; Semantics; Societies; Source; Testing; Time; Training; Twin Multiple Birth; Unified Medical Language System; United States National Institutes of Health; Universities; Update; Utah; Work; Writing; career; career development; computer program; convolutional neural network; deep learning; deep neural network; detector; experience; health care settings; improved; insight; large datasets; learning algorithm; machine learning model; mathematical model; multidisciplinary; neural network; skills; statistics; transfer learning

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.

候选人：凭借我在人工智能（博士）、公共卫生信息学方面的多学科背景