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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10842906
• 关键词: Address; Administrative Supplement; Adoption; Algorithms; Artificial Intelligence; Caring; Centers of Research Excellence; Clinic; Collaborations; Complex; Data; Data Science; Data Set; Databases; Devices; Dimensions; Documentation; Electronic Health Record; Ensure; Ethics; Fostering; Foundations; Fright; Goals; Grant; Healthcare; Home; Learning; Literature; Low income; Machine Learning; Methods; Modeling; Observational Study; Oncology; Outcome; Palliative Care; Parents; Patient Outcomes Assessments; Patient Self-Report; Patients; Pattern; Process; Readiness; Reporting; Reproducibility; Research; Research Personnel; Statistical Models; Structure; Time; Work; analysis pipeline; autoencoder; cancer care; cancer health disparity; care delivery; computer program; cost; data reuse; data sharing; design; evidence base; experience; feature extraction; generative adversarial network; health data; health practice; high dimensionality; improved; individual patient; insight; machine learning algorithm; machine learning method; multimodality; parent grant; patient portal; patient subsets; precision oncology; programs; prospective; remediation; simulation; skills; statistical and machine learning; survivorship; telehealth; tool; trial design; unsupervised learning

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： MATCHES（Making Telehealth Delivery of Cancer Care at Home Effective and Safe）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有利的结果，但大规模的试验 仅限于特定的情况，如姑息治疗或生存。采用受到限制， 有限的补偿。MATCHES中心将通过执行前瞻性研究来帮助弥补这一证据缺口。 试验和进行观察分析。数据将从远程医疗平台的多个层面进行整合， 患者门户、移动的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-精确护理提供-最终目标是将个体患者与 在适当的时候将诊所或远程保健支持的家庭护理结合起来， 所有这些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策展的数据集中观察到缺失数据，这也是 EHR和患者报告的健康数据。由于缺失数据的存在，MATCHES数据不 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 导致偏差和统计功效损失。如果一个亚组的患者更多， 很可能有缺失的数据。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工作，他们的经验将在数据和分析中代表不足， 从而损害结论的稳健性和普遍性。这些问题是公认的， 已经开发了统计文献和一系列工具，用合理的值来估算缺失数据 从概率模型中获得，并进行分析，认识到一些数据点是插补的。 但是，许多插补方法无法按比例放大到MATCHES数据中的维度， be robust鲁棒to different不同missing失踪data数据mechanisms机制.此外，没有关于如何检查丢失的 数据模式，特别是在高维特征空间中，如MATCHES。因此，在这 作为补充，我们提出并开发了基于机器学习的方法，这些方法将能够处理 维度特征矩阵、复杂的缺失模式和更一般的缺失机制。我们将 然后应用这些方法来检查复杂的缺失数据模式，并提供 为MATCHES计划的研究人员以及与其他人共享ML/AL应用程序做好了准备 远程医疗卓越研究中心（TRACE）我们还将提供分析管道， 帮助适当处理其他大规模多模态医疗保健数据集中的缺失数据。

项目成果

Honing in on the Hospital-at-Home Model.

• DOI: 10.1016/j.mcpdig.2023.06.015
• 发表时间: 2023-09
• 期刊: Mayo Clinic proceedings. Digital health
• 作者: Mullangi, Samyukta;Daly, Bobby
• 通讯作者: Daly, Bobby

Telemedicine as patient-centred oncology care: will we embrace or resist disruption?

远程医疗作为以患者为中心的肿瘤护理：我们会拥抱还是抵制颠覆？

• DOI: 10.1038/s41571-023-00796-5
• 发表时间: 2023
• 期刊: Nature reviews. Clinical oncology
• 作者: West,HowardJack;Bange,Erin;Chino,Fumiko
• 通讯作者: Chino,Fumiko