Modeling the Incompleteness and Biases of Health Data

对健康数据的不完备性和偏差进行建模 研究人员越来越多地致力于“挖掘”健康数据，以获取新的医学知识。不像 根据研究方案收集的实验数据，临床数据的主要作用是帮助 临床医生照顾病人，因此收集它的程序通常不是系统的。因此，丢失和/或 有偏见的数据可能会阻碍医学知识发现和数据挖掘工作。解决健康缺失问题的现有努力 数据推算通常只关注横截面相关性(例如，跨对象或跨 变量)，但忽略了自相关性(例如，跨时间点的相关性)。此外，他们经常把重点放在 模型的不完备性，但忽略了健康数据中的偏差。 对不完整性和偏差进行建模可能有助于更好地理解健康数据和更好地 支持临床决策。我们提出了一种新的基于偏差感知的缺失数据填补框架 横断面相关和自相关(BAMICA)，并利用临床笔记更好地向 否则将仅依赖结构化健康数据的方法。除了评估其归因于 准确性，我们将应用建议的框架来帮助下游任务，如预测建模 在不同的临床和队列研究数据集上的多个结果。 目的1介绍联合考虑截面相关性和自相关性的MICA框架。在……里面 目标2，我们将增强MICA以识别偏差(因此BAMICA)，以解释源于多个 根，如医疗保健流程，并将其用作输入丢失的健康数据的特征。这一增强 是通过一种新的循环神经网络体系结构实现的，该体系结构跟踪健康数据的演变 变量和偏差因素。在目标3中，我们将把非结构化的临床记录补充到结构化的健康数据中 一种新的基于记忆的图神经网络体系结构用于不完备性和偏差建模 网络。我们将应用图形神经网络来处理临床病历，以便学习正确的表示法。 作为存储器网络的输入，用于推算和下游预测建模任务。取决于 临床问题和数据可用性，并不是所有模块都可能需要。因此，我们建议的BAMICA框架 设计灵活，由可选择的模块组成，以满足上述部分或全部需求。 总之，我们的建议弥合了在联合建模不完备性和偏差方面的关键知识差距 健康数据，并利用非结构化的临床记录来补充和增强这种建模，以便更好地 支持预测性建模和临床决策。我们将通过以下方式来演示泛化能力 在四个大型临床和队列研究数据集上进行实验，并通过扩展到Emerge网络 横跨全国11个机构。我们将传播开放源码框架。有原则的和 该项目产生的灵活框架将带来显著的方法进步，并具有 对加强从健康数据中发现的直接影响。

Modeling the Incompleteness and Biases of Health Data Researchers are increasingly working to “mine” health data to derive new medical knowledge. Unlike experimental data that are collected per a research protocol, the primary role of clinical data is to help clinicians care for patients, so the procedures for its collection are not often systematic. Thus, missing and/or biased data can hinder medical knowledge discovery and data mining efforts. Existing efforts for missing health data imputation often focus on only cross-sectional correlation (e.g., correlation across subjects or across variables) but neglect autocorrelation (e.g., correlation across time points). Moreover, they often focus on modeling incompleteness but neglect the biases in health data. Modeling both the incompleteness and bias may contribute to better understanding of health data and better support clinical decision making. We propose a novel framework of Bias-Aware Missing data Imputation with Cross-sectional correlation and Autocorrelation (BAMICA), and leverage clinical notes to better inform the methods that will otherwise rely on structured health data only. In addition to evaluating its imputation accuracy, we will apply the proposed framework to assist in downstream tasks such as predictive modeling for multiple outcomes across a diverse range of clinical and cohort study datasets. Aim 1 introduces the MICA framework to jointly consider cross-sectional correlation and auto-correlation. In Aim 2, we will augment MICA to be bias-aware (hence BAMICA) to account for biases stemmed from multiple roots such as healthcare process and use them as features in imputing missing health data. This augmentation is achieved by a novel recurrent neural network architecture that keeps track of both evolution of health data variables and bias factors. In Aim 3, we will supplement unstructured clinical notes to structured health data for modeling incompleteness and biases using a novel architecture of graph neural network on top of memory network. We will apply graph neural networks to process clinical notes in order to learn proper representations as input to the memory networks for imputation and downstream predictive modeling tasks. Depending on the clinical problem and data availability, not all modules may be needed. Thus our proposed BAMICA framework is designed to be flexible and consists of selectable modules to meet some or all of the above needs. In summary, our proposal bridges a key knowledge gap in jointly modeling incompleteness and biases in health data and utilizes unstructured clinical notes to supplement and augment such modeling in order to better support predictive modeling and clinical decision making. We will demonstrate generalizability by experimenting on four large clinical and cohort study datasets, and by scaling up to the eMERGE network spanning 11 institutions nationwide. We will disseminate the open-source framework. The principled and flexible framework generated by this project will bring significant methodological advancement and have a direct impact on enhancing discovery from health data.