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.

项目摘要： 远程医疗研究（使家庭有效且安全的癌症护理远程医疗服务）远程医疗研究 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，以肿瘤学为中心的远程医疗可以取得有利的结果，但是大规模的试验 仅限于姑息治疗或生存等特定环境。收养已受到限制 限制报销。比赛中心将通过执行潜在的有助于弥补这一证据差距 试验和进行观察分析。数据将从远程医疗平台的多层集成， 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 肿瘤学范式 - 精确护理的交付 - 是将个别患者与 在适当的时候，基于诊所或远程医疗的家庭设定护理的最有益组合 - 所有这些都是基于动态可用数据的总数。这将通过应用数据科学来实现 方法（包括敏捷的试验设计和机器学习）对远程医疗的应用有限。 在策划的数据集中观察到丢失的数据，这也是 EHR和患者报告的健康数据。由于缺少数据，匹配数据不是 准备用于机器学习或人工智能应用程序，因为缺少数据的不适当处理可以 导致偏见和统计能力的丧失。偏见特别关注的是，如果患者的子组更多 可能遗漏了数据。例如，如果低收入患者更有可能跳过自我报告的结果 由于担心触发昂贵的工作，他们的经验在数据和分析中的代表性不足， 损害结论的鲁棒性和概括性。这些问题在 已经开发了统计文献和各种各样的工具来导入具有合理值的丢失数据 从概率模型中获得并执行分析，以认识到某些数据点是估算的。 但是，许多插补方法不能扩展到匹配数据中的维度，并且可能不会 对不同的数据机制保持鲁棒。此外，没有关于如何检查缺失的指导 数据模式系统地，尤其是在匹配中的高维特征空间中。因此 补充，我们建议并开发基于机器的学习方法，以便能够处理高级 尺寸特征矩阵，复杂的缺失模式和更普遍的缺失机制。我们将 然后应用这些方法来检查复杂的丢失数据模式，并提供了估算的数据集 为ML/AL应用程序准备了比赛计划的研究人员，并且要与他人共享 跨越远程医疗研究中心（Trace）。我们还将提供分析管道 在其他大规模多模式医疗保健数据集中，有助于适当处理丢失的数据。

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