EAGER: Patient Similarity Learning with Massive Clinical Data and Its Applications in Cohort Identification

EAGER：海量临床数据的患者相似性学习及其在队列识别中的应用

• 批准号: 1650723
• 负责人: Fei Wang
• 金额: $ 29.99万
• 依托单位: Joan and Sanford I. Weill Medical College of Cornell 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1650723&HistoricalAwards=false
• 关键词: EAGER; Patient; Similarity; Learning; Massive

The rapid adoption of Electronic Health Records (EHRs) across the U.S. healthcare systems coupled with the capability of linking EHRs to research biorepositories provides a unique opportunity for conducting large-scale Precision Medicine research. A critical step to make such research possible is identification of cohorts by defining inclusion and exclusion criteria that algorithmically select sets of patients based on available clinical data. For most of the existing research, the criteria for generating those patient cohorts are defined manually, which makes the entire process slow, labor intensive and not scalable. This project develops patient similarity learning algorithms to enable automatic cohort identification, which will accelerate the research of precision medicine.The massive clinical data around patients are highly heterogeneous and sparse. Although there are some patient similarity learning algorithms, they typically work with a single type of patient data (e.g., just using diagnosis information in patient EHR) and cannot handle those challenges mentioned above effectively. This project develops advanced patient similarity learning algorithms by 1) learning composite patient similarities through a refinement process from multiple base similarity measures, with each base similarity being evaluated from a specific source of patient data or a specific form of patient representation; and 2) integrating information from multiple related auxiliary domains, such as drug, disease, and genomic information. Those information effectively regularizes the patient similarity learning process and makes it less sensitive to data sparsity.

电子健康记录(EHR)在整个美国医疗保健系统中的迅速采用，加上将EHR链接到研究生物信息库的能力，为开展大规模精密医学研究提供了独特的机会。使这类研究成为可能的一个关键步骤是通过定义纳入和排除标准来确定队列，这些标准根据现有的临床数据通过算法选择患者组。在现有的大多数研究中，生成这些患者队列的标准都是手动定义的，这使得整个过程变得缓慢、劳动密集型和不可扩展。本项目开发患者相似度学习算法，实现自动队列识别，将加速精准医学的研究。尽管有一些患者相似性学习算法，但它们通常使用单一类型的患者数据(例如，仅使用患者电子病历中的诊断信息)，并且无法有效地应对上述挑战。该项目通过以下方式开发先进的患者相似性学习算法：1)通过从多个基本相似性度量中通过细化过程学习复合患者相似性，其中每个基本相似性是从特定的患者数据来源或特定形式的患者表示进行评估；以及2)集成来自多个相关辅助领域的信息，例如药物、疾病和基因组信息。这些信息有效地规范了患者的相似性学习过程，并使其对数据稀疏性不那么敏感。

项目成果

Polyadic Regression and its Application to Chemogenomics

• DOI: 10.1137/1.9781611974973.9
• 发表时间: 2017
• 作者: Ioakeim Perros;Fei Wang;Ping Zhang;Peter Walker;R. Vuduc;Jyotishman Pathak;Jimeng Sun

Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction

• DOI: 10.1609/aaai.v31i1.10718
• 发表时间: 2017-02
• 作者: Bo Jin;Haoyu Yang;Cao Xiao;Ping Zhang;Xiaopeng Wei;Fei Wang

Structural Deep Embedding for Hyper-Networks

• DOI: 10.1609/aaai.v32i1.11266
• 发表时间: 2017-11
• 期刊: ArXiv
• 作者: Ke Tu;Peng Cui;Xiao Wang;Fei Wang;Wenwu Zhu

Robust finite mixture regression for heterogeneous targets

• DOI: 10.1007/s10618-018-0564-z
• 发表时间: 2018-04
• 期刊: Data Mining and Knowledge Discovery
• 影响因子: 4.8
• 作者: Jian Liang;Kun Chen;Ming Lin;Changshui Zhang;Fei Wang

Dynamical Origins of Distribution Functions

• DOI: 10.1145/3292500.3330842
• 发表时间: 2019-07
• 期刊: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Chengxi Zang;Peng Cui;Wenwu Zhu;Fei Wang