III: Small: A New Approach to Latent Space Learning with Diversity-Inducing Regularization and Applications to Healthcare Data Analytics

III：小型：具有多样性诱导正则化的潜在空间学习新方法及其在医疗保健数据分析中的应用

• 批准号: 1617583
• 负责人: Eric Xing
• 金额: $ 49.94万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617583&HistoricalAwards=false
• 关键词: III; Small; New; Approach; Latent

Latent variable models (LVMs), which extract hidden information, such as topics, themes, or disease patterns, from raw data, play an important role in electronic health record (EHR) management and applications. With the dramatic increase of the volume and complexity of EHR data, current LVMs face several new challenges, including inadequacy in capturing rare patterns existing in only small number of patients in a population (also known as long tail patterns), redundancy amongst patterns being discovered, and low computational efficiency, which all seriously impair the value of EHR data in driving high-quality personalized medicine. There is a critical need in developing new methods to transform conventional LVMs to ones that can circumvent such limitations so that the EHR data can be more effectively and reliably used for healthcare applications. This project addresses this need and develops a new technique known as "diversity-inducing machine learning models", which promote rare patterns and condense redundant patterns, at high computational efficiency, to enable more effective pattern discovery and knowledge extraction from complex and heterogeneous (e.g., textual, image, and time series) EHR data. Specifically, this project contains the following research components: 1. Develop a new regularized LVM learning framework that allows the basis of the latent space to favor a more diversity-inducing geometry and less redundancy, thereby accomplish long-tail pattern coverage and better interpretability for both Euclidean and Hilbert space settings. 2. Develop a diversity-promoting Bayesian LVM learning framework that enables efficient inference of posteriors probability distributions to facilitate quantization of uncertainty and alleviate over fitting. 3. Theoretically analyze the diversity-inducing techniques proposed in 1 and 2 to understand how these techniques affect the generalization errors in supervised LVMs, posterior contraction rate in unsupervised LVMs, and the information geometry of the distributions induced by LVMs. 4. Apply the diversified LVMs to healthcare applications. This project also provides rich opportunities for multi-disciplinary education and research training, at both undergraduate, graduate, and professional levels.

潜在变量模型（LVM）从原始数据中提取隐藏的信息，例如主题，主题或疾病模式，在电子健康记录（EHR）管理和应用中发挥重要作用。随着EHR数据的数量和复杂性的急剧增加，当前的LVM面临着几个新的挑战，包括在捕获人群中少数患者中存在的稀有模式不足（也称为长尾巴模式），发现模式之间的冗余性，低计算效率，以及低的计算效率，这些都严重损害了EHR数据在驾驶高级药物中的价值。开发新方法将常规LVM转换为可以规避此类局限性的新方法的迫切需要，以便可以更有效，可靠地用于医疗保健应用程序。该项目解决了这一需求，并开发了一种称为“诱导多样性的机器学习模型”的新技术，该技术以高计算效率促进了稀有模式和凝结冗余模式，以使更有效的模式发现和知识从复杂和异构（例如，文本，图像和时间序列）中提取更有效的模式发现和知识。具体而言，该项目包含以下研究组件：1。开发一个新的正规LVM学习框架，该框架可以允许潜在空间的基础倾向于诱发多样性的几何形状和较小的冗余，从而实现了长尾模式覆盖范围，并为欧几里得和希尔伯特太空设置提供了更好的解释性。 2。开发一个促进多样性的贝叶斯LVM学习框架，该框架能够有效推断后期概率分布，以促进量化不确定性和减轻拟合的量化。 3。理论上分析了1和2中提出的多样性诱导技术，以了解这些技术如何影响受监督LVM的泛化错误，无监督的LVM中的后验收缩率以及LVM诱导的分布的信息几何学。 4。将多元化的LVM应用于医疗保健应用。该项目还为本科，研究生和专业水平的多学科教育和研究培训提供了丰富的机会。