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: 10589998
• 负责人: Ye Ye
• 金额: $ 23.65万
• 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/10589998
• 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; Doctor of Philosophy; Epidemic; Epidemiology; Future; Goals; Grant; Health; Healthcare Systems; Heterogeneity; Influenza; Institution; Investigation; Investments; Knowledge; Lead; 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; base; career; career development; computer program; convolutional neural network; deep learning; deep neural network; detector; experience; health care settings; improved; insight; large datasets; learning algorithm; mathematical model; multidisciplinary; neural network; skills; statistics; transfer learning; usability

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：拥有人工智能（博士）、公共卫生信息学等多学科背景 （MS）、流行病学和健康统计学（MS）和预防医学（医学学士），我的职业 目标是成为一名在人工智能和人工智能交叉领域工作的独立调查员 生物医学，最初特别强调机器学习和公共卫生。 培训计划：我的K99/R00培训计划强调机器学习、深度学习和 科学沟通技能（演讲、撰写文章和资助申请），这将补充 我目前在人工智能、统计学、医学和公共卫生方面的优势。我有很强的 辅导团队。我的导师，博士。迈克尔·贝西奇（小学）、格雷戈里·库珀、黄恒和迈克尔 瓦格纳，他们都在研究和职业生涯发展方面经验丰富。 研究计划：我提出的 K99/R00 资助的研究目标是增加 可计算的生物医学知识，即以计算机可解释的形式表示的知识 例如贝叶斯网络和神经网络。我将这种表示称为模型。虽然型号 可以在其他设置中重新使用，可能会导致性能损失，甚至更多 有问题的是，模型所需的数据与可用数据之间根本不匹配 新的设置使得它们无法重复使用。迁移学习领域开发的算法 将知识从一种环境转移到另一种环境。迁移学习是机器学习的一个子领域， 明确区分源设置（具有我们想要重用的模型）和 目标设定，数据不足以从数据中推导出模型，因此需要重用模型 从源设置。我建议开发和评估几种贝叶斯网络迁移学习（BN- TL）算法和卷积神经网络迁移学习算法。我的具体研究目标 目标是：(1) 进一步开发和评估 BN-TL，以在整个医疗保健领域共享可计算知识 设置; (2) 开发和评估 BN-TL，以随着时间的推移更新可计算知识； (3) 开发和 评估结合异构场景知识的深度迁移学习算法。我会做 这项研究用于自动检测流感等传染病病例的模型。 影响：拟议的研究利用了我之前开发的大型数据集；因此我 期望快速获得对如何从网络中共享病例检测模型产生直接影响的结果 从最初出现流行病的地区转移到希望实现最佳病例检测的另一个地点 尽早具备能力。更一般地说，它将带来对机器学习增强的洞察力 生物医学知识共享和更新。这笔培训补助金将使我做好独立工作的准备 领导开发计算解决方案以满足未来 R01 项目的生物医学需求。