Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records

项目摘要/摘要 该项目提出了在电子健康记录(EHR)中表示数据的新方法，以提高Pre 患者结局的可判断性建模和解释。电子病历数据提供了一个很有希望的机会 了解临床决策和患者病情如何随着时间的推移相互作用，从而改善患者的健康状况。fl。 然而，电子病历数据很难用于预测建模，因为它们包含各种数据类型(与之相反)。 连续的、明确的、文本等)、它们的纵向性质、确定的高数量的非随机遗漏 测量，以及其他方面的担忧。此外，患者的预后通常有不同的原因和复发。 查询从几个临床实验室措施和患者访问中合成的信息。核心挑战 当务之急是克服电子病历中的数据表示与不足的假设之间的不匹配- ING常用的统计和机器学习(ML)方法。为此，本项目提出了新颖的 基于包装器的方法，用于从电子病历数据中学习信息特征。这两种方法都提出了专门的 操作员处理顺序数据、时间延迟和可变交互，并有能力发现 影响患者结局的潜在临床规则/决定。重要的是，这两种方法也都产生了档案 表示复杂性和精确度之间的最佳折衷的可能模型，这有助于模型 释义。这些方法的进步是通过将丰富的数据操作集编码为节点来实现的 在有向无环图中，利用多目标优化方法对图的结构进行优化。中环 本研究的假设是，多目标优化可以学习有效的数据表示方法 电子病历能够产生准确的、解释性的患者结局模型。初步工作表明，这些 方法可以有效地学习低阶数据表示，从而提高几种状态的预测能力- 最先进的ML方法。这项技术在高维生物医学中表现出良好的缩放特性 数据。目标1(K99)是开发一种与现有最大似然方法配对的多目标特征工程方法 通过从原始数据构建新要素并使用来自 训练好的模型用于指导特征构建。在目标2(K99)中，该方法被应用于形成预测模型 使用纵向EHR数据评估心脏病和心力衰竭的风险。由此产生的模型将是 在导师的帮助下进行预测，以便将预测转化为临床建议。对于目标3(R00)， 第二种方法是使用类似的框架来优化现有的神经网络方法 以便在保持精确度的同时尽可能简化其结构。目标4(R00)的目标是 识别有再次入院风险的医院患者，并提出护理点策略以减轻这种风险 风险。通过将建议的方法应用于从 宾夕法尼亚大学医院、盖辛格健康系统和公开可用的EHR数据库。

Project Summary/Abstract This project proposes new methods for representing data in electronic health records (EHR) to improve pre- dictive modeling and interpretation of patient outcomes. EHR data offer a promising opportunity for advancing the understanding of how clinical decisions and patient conditions interact over time to influence patient health. However, EHR data are difficult to use for predictive modeling due to the various data types they contain (con- tinuous, categorical, text, etc.), their longitudinal nature, the high amount of non-random missingness for certain measurements, and other concerns. Furthermore, patient outcomes often have heterogenous causes and re- quire information to be synthesized from several clinical lab measures and patient visits. The core challenge at hand is overcoming the mismatch between data representations in the EHR and the assumptions underly- ing commonly used statistical and machine learning (ML) methods. To this end, this project proposes novel wrapper-based methods for learning informative features from EHR data. Both methods propose specialized operators to handle sequential data, time delays, and variable interactions, and have the capacity to discover underlying clinical rules/decisions that affect patient outcomes. Importantly, both methods also produce archives of possible models that represent the best trade-offs between complexity and accuracy, which assists in model interpretation. These method advances are made possible by encoding a rich set of data operations as nodes in a directed acyclic graph, and optimizing the graph structures using multi-objective optimization. The central hypothesis of this research is that multi-objective optimization can learn effective data representations from the EHR to produce accurate, explanatory models of patient outcomes. Preliminary work has shown that these methods can effectively learn low-order data representations that improve the predictive ability of several state- of-the-art ML methods. This technique demonstrates good scaling properties with high-dimensional biomedical data. Aim 1 (K99) is to develop a multi-objective feature engineering method that pairs with existing ML methods to iteratively improve their performance by constructing new features from the raw data and using feedback from the trained model to guide feature construction. In Aim 2 (K99), this method is applied to form predictive models of the risk of heart disease and heart failure using longitudinal EHR data. The resultant models will be inter- preted with the help of mentors in order to translate predictions into clinical recommendations. For Aim 3 (R00), a second method is proposed that uses a similar framework to optimize existing neural network approaches in order to simplify their structure as much as possible while maintaining accuracy. The goal of Aim 4 (R00) is to identify hospital patients who are at risk of readmission and propose point-of-care strategies to mitigate that risk. This goal is facilitated through the application of the proposed methods to patient data collected from the Hospital of the University of Pennsylvania, the Geisinger Health System, and publicly available EHR databases.