Big Data Methods for Comprehensive Similarity based Risk Prediction

项目摘要 电子健康记录（EHR）提供了关于代表性人群的丰富数据源， 充分利用，以提高临床决策。临床决策的传统方法开始于 基于主题知识识别相关生物标志物，然后进行详细但 仅使用这些生物标志物的分析有限。正如目前的科学文献所指出的，许多人 疾病共有复杂的病因学基础并表现出相关的疾病进展。因此有 期望使用用于患者相似性的综合患者数据。该提案的重点是， 从当前完整的患者特征中获得患者相似性的综合评分 可用，包括但不限于1）人口统计学相似性; 2）遗传相似性; 3）临床表型 相似性; 4）治疗相似性;以及5）麻烦相似性（这里麻烦定义为所有可用属性 当某些方面可能重叠和相互作用时。我们 将优化信息融合和任务相关的特征选择，以评估患者的临床相似性 风险预测由于目前还不存在一个管道，能够提取可执行完整的 患者决定因素数据，以实现上述研究目标，我们建议首先提供一个开放的， 基于广泛使用的临床数据标准OMOP的源数据准备管道 （观察性医学成果伙伴关系）通用数据模型（CMD）5.2版。此外，为了减轻 临床数据中常见的缺失和稀疏性挑战，我们描述了第一次尝试代表 患者稀疏的临床信息缺失，包括诊断信息、用药数据， 治疗干预，具有固定长度的特征向量（即Patient 2 Vec）。该项目有四个具体 目标。目的1是开发一个临床数据处理管道，用于协调来自多个 源转换为基于标准的统一数据表示，并评估其效率，互操作性， 和准确性。目标2是利用一种强大的机器学习技术Document 2 Vec， 语言处理文献，创建一个开源的Patient 2 Vec框架， 患者的信息数字表示。目标3是开发一个统一的机器学习临床- 优化患者相似性融合（OptPSF）的结果预测框架， 医学协变量与来自Patient 2 Vec（目标2）的患者数值表示，用于改善 临床风险预测目标4是评估我们的相似性框架来预测1）终末期风险 一般EHR患者人群中的肾脏疾病（ESKD）和2）慢性肾脏疾病患者的死亡风险 肾病（CKD）。

Project Summary Electronic health records (EHR) provide rich source of data about representative populations and are yet to be fully utilized to enhance clinical decision-making. Conventional approaches in clinical decision-making start with the identification of relevant biomarkers based on subject-matter knowledge, followed by detailed but limited analysis using these biomarkers exclusively. As the current scientific literature indicates, many human disorders share a complex etiological basis and exhibit correlated disease progression. Therefore, it is desirable to use comprehensive patient data for patient similarity. This proposal focuses on deriving a comprehensive and integrated score of patient similarity from complete patient characteristics currently available, including but not limited to 1) demographic similarity; 2) genetic similarity; 3) clinical phenotype similarity; 4) treatment similarity; and 5) exposome similarity (here exposome defined as all available attributes of the living environment an individual is exposed to), when some of the aspects may overlap and interact. We will optimize information fusion and task-dependent feature selection for assessing patient similarity for clinical risk prediction. Since currently there does not exist a pipeline that is able to extract executable complete patient determinant data, to achieve the research goal described above, we propose first deliver an open- source data preparation pipeline that is based on a widely used clinical data standard, the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CMD) version 5.2. Moreover, to mitigate common missingness and sparsity challenges in clinical data, we describe the first attempt to represent patients' sparse clinical information with missingness, including diagnosis information, medication data, treatment intervention, with a fixed-length feature vector (i.e. the Patient2Vec). This project has four specific aims. Aim 1 is to develop a clinical data processing pipeline for harmonizing patient information from multiple sources into a standards-based uniformed data representation and to evaluate its efficiency, interoperability, and accuracy. Aim 2 is to leverage a powerful machine learning technique, Document2Vec, from the natural language processing literature, to create an open-source Patient2Vec framework for the derivation of informative numerical representations of patients. Aim 3 is to develop a unified machine learning clinical- outcome-prediction framework for Optimized Patient Similarity Fusion (OptPSF) that integrates traditional medical covariates with the derived numerical patient representations from Patient2Vec (Aim 2) for improved clinical risk prediction. Aim 4 is to evaluate our similarity framework for predicting 1) the risk of end-stage kidney disease (ESKD) in general EHR patient population and 2) the risk of death among patients with chronic kidney disease (CKD).