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: Making Telehealth Delivery of Cancer Care at Home Effective and Safe - Addressing missing data in the MATCHES study to improve ML/AI readiness
批准号:
10842906
负责人:
MICHAEL J MORRIS
金额:
$35.39万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-19 至 2027-07-31
关键词:
AddressAdministrative SupplementAdoptionAlgorithmsArtificial IntelligenceCaringCenters of Research ExcellenceClinicCollaborationsComplexDataData ScienceData SetDatabasesDevicesDimensionsDocumentationElectronic Health RecordEnsureEthicsFosteringFoundationsFrightGoalsGrantHealthcareHomeLearningLiteratureLow incomeMachine LearningMethodsModelingObservational StudyOncologyOutcomePalliative CareParentsPatient Outcomes AssessmentsPatient Self-ReportPatientsPatternProcessReadinessReportingReproducibilityResearchResearch PersonnelStatistical ModelsStructureTimeWorkanalysis pipelineautoencodercancer carecancer health disparitycare deliverycomputer programcostdata reusedata sharingdesignevidence baseexperiencefeature extractiongenerative adversarial networkhealth datahealth practicehigh dimensionalityimprovedindividual patientinsightmachine learning algorithmmachine learning methodmultimodalityparent grantpatient portalpatient subsetsprecision oncologyprogramsprospectiveremediationsimulationskillsstatistical and machine learningsurvivorshiptelehealthtooltrial designunsupervised learning
中文摘要
项目总结:
英文摘要
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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1016/j.mcpdig.2023.06.015
发表时间:
2023-09
期刊:
Mayo Clinic proceedings. Digital health
影响因子:
--
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
影响因子:
--
作者:
[West,HowardJack, Bange,Erin, Chino,Fumiko]
通讯作者:
Chino,Fumiko
Pragmatic Trial
-
批准号:10454673
-
项目类别:
-
资助金额:$22.31万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
MATCHES: Making Telehealth Delivery of Cancer Care at Home Effective and Safe
-
批准号:10673980
-
项目类别:
-
资助金额:$121.42万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
Administrative Core
-
批准号:10454671
-
项目类别:
-
资助金额:$29.14万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
Pragmatic Trial
-
批准号:10674001
-
项目类别:
-
资助金额:$31.91万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
Administrative Core
-
批准号:10912840
-
项目类别:
-
资助金额:$35.39万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
MATCHES: Making Telehealth Delivery of Cancer Care at Home Effective and Safe
-
批准号:10454670
-
项目类别:
-
资助金额:$123.9万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
Administrative Core
-
批准号:10673982
-
项目类别:
-
资助金额:$26.43万
-
财政年份:2022
-
负责人:MICHAEL J MORRIS
-
依托单位:
Clinical Qualification of Imaging and Fluid-Based Tumor Monitoring Biomarkers for Metastatic Castration Resistant Prostate Cancer
-
批准号:9974088
-
项目类别:
-
资助金额:$71.97万
-
财政年份:2020
-
负责人:MICHAEL J MORRIS
-
依托单位:
Clinical Qualification of Imaging and Fluid-Based Tumor Monitoring Biomarkers for Metastatic Castration Resistant Prostate Cancer
-
批准号:10447573
-
项目类别:
-
资助金额:$68.99万
-
财政年份:2020
-
负责人:MICHAEL J MORRIS
-
依托单位:
Clinical Qualification of Imaging and Fluid-Based Tumor Monitoring Biomarkers for Metastatic Castration Resistant Prostate Cancer
-
批准号:10868060
-
项目类别:
-
资助金额:$97.49万
-
财政年份:2020
-
负责人:MICHAEL J MORRIS
-
依托单位:
Radioimmunotherapy for Prostate Cancer
-
批准号:6677074
-
项目类别:
-
资助金额:$13.47万
-
财政年份:2003
-
负责人:MICHAEL J MORRIS
-
依托单位:
Radioimmunotherapy for Prostate Cancer
-
批准号:6937710
-
项目类别:
-
资助金额:$13.47万
-
财政年份:2003
-
负责人:MICHAEL J MORRIS
-
依托单位:
Radioimmunotherapy for Prostate Cancer
-
批准号:6797363
-
项目类别:
-
资助金额:$13.47万
-
财政年份:2003
-
负责人:MICHAEL J MORRIS
-
依托单位:
Radioimmunotherapy for Prostate Cancer
-
批准号:7115714
-
项目类别:
-
资助金额:$13.47万
-
财政年份:2003
-
负责人:MICHAEL J MORRIS
-
依托单位:
海外基金