MATCHES: Making Telehealth Delivery of Cancer Care at Home Effective and Safe - Addressing missing data in the MATCHES study to improve ML/AI readiness

MATCHES：使远程医疗在家中有效且安全地提供癌症护理 - 解决 MATCHES 研究中缺失的数据，以提高 ML/AI 的准备情况

基本信息

批准号：
10842906
负责人：
MICHAEL J MORRIS
金额：
$ 35.39万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-19 至 2027-07-31
项目状态：
未结题

项目摘要

Project Summary: The MATCHES (Making Telehealth Delivery of Cancer Care at Home Effective and Safe) Telehealth Research Center aims to build the evidence base necessary to establish best practices for telehealth-enabled cancer care. Prior work demonstrates that oncology-focused telehealth can achieve favorable outcomes, but large-scale trials have been limited to specific contexts like palliative care or survivorship. Adoption has been constrained by restricted reimbursement. The MATCHES Center will help remediate this evidence gap by executing prospective trials and conducting observational analyses. Data will be integrated from multi-layers from telehealth platforms, patient portals, mobile tracking devices, and the electronic health record (EHR). This will help develop a new paradigm in oncology—precision care delivery—with the ultimate goal of matching individual patients with the most beneficial combination of clinic-based or telehealth-supported home-setting care at the appropriate time— all based on the totality of dynamically available data. This will be accomplished by applying data science methods—including nimble trial designs and machine learning—that have had limited application to telehealth. Missing data have been observed in the MATCHES curated data sets, which is also a common issue of both EHR and patient-reported health data. Due to the presence of missing data, the MATCHES data is not ready for machine learning or artificial intelligence applications as inappropriate handling of missing data can lead to both bias and loss of statistical power. Bias is particularly concerning if a subgroup of patients is more likely to have missing data. For example, if low-income patients are more likely to skip self-reported outcomes for fear of triggering costly work-up, their experience will be underrepresented in the data and analysis, compromising the robustness and generalizability of conclusions. These issues are well-recognized in the statistical literature and a wide array of tools have been developed to impute missing data with plausible values obtained from a probabilistic model and perform analyses recognizing that some data points are imputed. However, many imputation methods do not scale up to the dimensions in the MATCHES data, and they may not be robust to differentmissing data mechanisms. Additionally, there is no guidance on how to examine the missing data patterns systematically, especially in the high-dimensional feature space as in MATCHES. Hence in this supplement, we propose and develop machine-learning-based approaches that will be able to handle a high- dimensional feature matrix, complex patterns of missingness, and more general missing mechanisms. We will then apply these methods to examine the complex missing data patterns and provide imputed data sets that are ready for ML/AL applications both for the researchers of the MATCHES program and to be shared with others across the Telehealth Research Centers of Excellence (TRACE). We will also provide analysis pipelines that will help appropriately handle missing data in other large-scale multi-modality healthcare data sets.

项目概要：比赛（使远程医疗在家中提供有效且安全的癌症护理）远程医疗研究该中心旨在建立必要的证据基础，以建立远程医疗癌症护理的最佳实践。先前的工作表明，以肿瘤学为重点的远程医疗可以取得良好的结果，但大规模试验仅限于姑息治疗或幸存者等特定情况。 MATCHES 中心将通过执行前瞻性措施来帮助弥补这一证据差距。试验和进行观察分析将来自远程医疗平台的多层数据进行整合，患者门户、移动跟踪设备和电子健康记录（EHR）这将有助于开发新的。肿瘤学的范例——精准医疗服务——最终目标是将个体患者与在适当的时间将基于诊所或远程医疗支持的家庭护理的最有益组合—— 一切都基于动态可用数据的整体，这将通过应用数据科学来实现。方法（包括灵活的试验设计和机器学习）在远程医疗中的应用有限。在 MATCHES 策划的数据集中观察到数据缺失，这也是常见问题 EHR 和患者报告的健康数据由于存在缺失数据，因此 MATCHES 数据不完整。为机器学习或人工智能应用做好准备，因为对丢失数据的不当处理可能会导致导致偏差和统计功效的丧失，如果一个患者亚组更多，则偏差尤其令人担忧。例如，如果低收入患者更有可能跳过自我报告的结果。由于担心引发昂贵的检查，他们的经验在数据和分析中将被低估，这些问题在结论的稳健性和普遍性上得到了充分认识。已经开发出统计文献和各种工具来用合理的值来估算缺失的数据从概率模型获得并执行分析，认识到某些数据点是估算的。但是，许多插补方法无法扩展到 MATCHES 数据中的维度，并且它们可能不会对不同的缺失数据机制具有鲁棒性此外，没有关于如何检查缺失数据的指导。系统地分析数据模式，特别是在高维特征空间中，例如 MATCHES。作为补充，我们提出并开发基于机器学习的方法，这些方法将能够处理高维度特征矩阵、复杂的缺失模式以及更一般的缺失机制。然后应用这些方法来检查复杂的缺失数据模式并提供估算数据集为 MATCHES 项目的研究人员和与其他人共享的 ML/AL 应用程序做好准备我们还将提供跨远程医疗卓越研究中心 (TRACE) 的分析管道。帮助正确处理其他大规模多模态医疗数据集中的缺失数据。