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

使用电子健康记录对患者结果进行可重复预测的多目标表示学习方法

基本信息

批准号：
10684907
负责人：
William La Cava
金额：
$ 23.66万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10684907
关键词：
Address Affect Archives Area Automobile Driving Cardiovascular Diseases Clinical Communities Complex Couples Data Data Reporting Data Set Databases Development Disease Disease Progression Electronic Health Record Engineering Feedback Goals Graph Hand Health Health Sciences Health system Heart Diseases Heart failure Hospitalization Hospitals Inpatients Knowledge Learning Machine Learning Maps Measurement Measures Mentors Methodology Methods Mining Modeling Nature Outcome Pathology Patient Care Patient-Focused Outcomes Patients Pennsylvania Performance Pharmaceutical Preparations Phase Population Process Property Protocols documentation Quality of life Recommendation Replacement Arthroplasty Research Research Personnel Structure Techniques Testing Text Time Training Translating University Hospitals Visit Work care costs cluster computing data complexity deep learning deep neural network design disease diagnosis disorder subtype gradient boosting heart disease risk high dimensionality hospital readmission improved insight learning strategy machine learning method machine learning model network architecture neural network novel open source tool operation patient health information point of care predictive modeling readmission rates readmission risk risk mitigation risk prediction model statistical and machine learning temporal measurement

项目摘要

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.

项目概要/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，以改善预 EHR 数据的预测建模和解释为推进提供了一个充满希望的机会。了解临床决策和患者状况如何随着时间的推移相互作用以影响患者的健康。然而，由于 EHR 数据包含各种数据类型，因此很难用于预测建模（反之亦然）。连续的、分类的、文本等），它们的纵向性质，某些特定的大量非随机缺失此外，患者的结果往往有不同的原因和重新考虑。获取从多种临床实验室测量和患者就诊中综合的信息是核心挑战。目前的任务是克服电子病历中的数据表示与潜在假设之间的不匹配- 为此，该项目提出了新颖的统计和机器学习（ML）方法。用于从 EHR 数据中学习信息特征的基于包装的方法这两种方法都提出了专门的方法。操作员处理顺序数据、时间延迟和变量交互，并有能力发现重要的是，这两种方法也会产生档案。代表复杂性和准确性之间最佳权衡的可能模型，这有助于模型这些方法的进步是通过将一组丰富的数据操作编码为节点而实现的。在有向无环图中，并使用多目标优化来优化图结构。这项研究的假设是多目标优化可以从中学习有效的数据表示初步工作表明，电子病历能够产生准确的、解释性的患者结果模型。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 方法在高维生物医学方面展示了良好的缩放特性。目标 1 (K99) 是开发一种与现有 ML 方法配对的多目标特征工程方法。通过从原始数据构建新特征并使用反馈来迭代提高其性能在目标 2 (K99) 中，应用该方法来形成预测模型。使用纵向 EHR 数据评估心脏病和心力衰竭的风险。在导师的帮助下进行预测，以便将预测转化为临床建议。提出了第二种方法，使用类似的框架来优化现有的神经网络方法为了在保持准确性的同时尽可能简化其结构。 Aim 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通过将所提出的方法应用于从患者收集的数据来促进这一目标。宾夕法尼亚大学医院、Geisinger 健康系统和公开的 EHR 数据库。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.

基于儿科心电图的深度学习来预测左心室功能障碍和重构。

DOI：
发表时间：
2024-03-19
期刊：
影响因子：
37.8
作者：
Mayourian, Joshua;La Cava, William G;Vaid, Akhil;Nadkarni, Girish N;Ghelani, Sunil J;Mannix, Rebekah;Geva, Tal;Dionne, Audrey;Alexander, Mark E;Duong, Son Q;Triedman, John K
通讯作者：
Triedman, John K

Interpretation of machine learning predictions for patient outcomes in electronic health records.

机器学习对电子健康记录中患者结果的预测的解释。