Developing a Childhood Asthma Risk Passive Digital Marker
开发儿童哮喘风险被动数字标记
基本信息
- 批准号:10571461
- 负责人:
- 金额:$ 16.2万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-02-01 至 2023-04-01
- 项目状态:已结题
- 来源:
- 关键词:Active LearningAddressAdultAgeAlgorithmsAsthmaAwardBiometryBirthBlindedBlood TestsChildChildhood AsthmaClinicalClinical ResearchDataDetectionDevelopmentDiagnosisDigital biomarkerDiseaseEarly DiagnosisEarly treatmentElectronic Health RecordEnrollmentEpidemiologistEpidemiologyEvaluationEvaluation StudiesFellowshipFutureGoalsGrantHealthIndianaIndividualInterventionIntuitionKnowledgeLogistic RegressionsMachine LearningMedicalMedical HistoryMentored Research Scientist Development AwardMentorsModelingMorbidity - disease rateMothersNational Heart, Lung, and Blood InstituteNatural HistoryNursery SchoolsOnline SystemsPathway interactionsPatient CarePatient-Focused OutcomesPerformancePhenotypePhysiciansPostdoctoral FellowPredictive AnalyticsPrognosisPrognostic FactorProxyRandom AllocationRandomizedResearchRiskSample SizeSchool-Age PopulationScientistSpirometrySymptomsTestingTimeTrainingTraining ActivityTranslatingUnited StatesUnited States National Institutes of HealthValidationcareerclinical decision supportclinical decision-makingclinical practicecohortdesigndigitaldisorder riskeffectiveness testingefficacious interventionefficacy evaluationexperiencehealth information technologyimplementation researchimprovedinsightmachine learning algorithmmachine learning methodmultidisciplinarynovelpediatricianpersonalized carepersonalized medicinepoint of careprimary caregiverprognosticprognostic signaturerandomized, clinical trialsskillssupport toolstooltranslational scientisttreatment choiceusability
项目摘要
PROJECT SUMMARY/ABSTRACT
Dr. Arthur Owora is a biostatistician and quantitative epidemiologist whose long-term career goal is to translate
prognostic research into clinical practice by designing and testing the effectiveness of intuitive clinical decision
support tools. This goal is predicated on the notion that applying novel biostatistical and machine learning (ML)
methodologies to increasingly available electronic health record (EHR) prognostic data can generate predictive
analytics and insights regarding disease risk. Clinicians can then use such insights for effective clinical decision-
making at point-of-care, including more proactive and personalized care, for improved patient-centered
outcomes. This is directly responsive to NIH National Heart, Lung, and Blood Institute’s strategic objective to
“Optimize clinical and implementation research to improve health and reduce disease.”
To achieve his long-term goal, Dr. Owora will leverage his graduate training in biostatistics and epidemiology,
post-doctoral fellowship in the modeling of developmental origins of disease, as well as previous prognostic
research experience to transition to research independence as a translational scientist. To this end, he requires
additional training in how to apply novel biostatistical and ML methodologies to develop digital clinical decision-
support tools, and 2) implement and evaluate the efficacy of such tools in clinical settings.
This proposal describes a 4-year project to develop and determine the usability, acceptability, feasibility, and
preliminary efficacy of a childhood asthma Passive Digital Marker for early disease detection. Here, a Passive
Digital Marker (PDM) refers to a ML algorithm that can be used to retrieve and synthesize pre-existing
‘Passively’ collected mother/child dyad prognostic data (i.e., medical history) at ages 0-3 years in ‘Digital’ EHR
to provide an objective and quantifiable ‘Marker’ of a child’s asthma risk and phenotype at ages 6-10 years.
Proposed specific aims build on Dr. Owora’s ongoing prognostic research to: (1) develop and evaluate the
predictive performance of a childhood asthma PDM, compared to a Pediatric Asthma Risk Score (as a proxy for
standard practice), and (2) determine the usability, acceptability, feasibility, and preliminary efficacy of the
childhood asthma PDM among pediatricians.
To address these objectives, Dr. Owora proposes training activities that include didactic and experiential
learning to build expertise in the development, implementation, and evaluation of the childhood asthma PDM in
clinical settings. These training activities will be supported by a strong multidisciplinary team of mentors:
Richard Holden (Translational Scientist in Health Information Technology), Eneida Mendonca (Pediatrician and
Medical Informatician), Robert Tepper (Physician-Scientist and Pulmonologist), Malaz Boustani (Physician and
Implementation Scientist), and Douglas Landsittel (Biostatistician and Bioinformatician).
With the preliminary data generated, new skills, and expertise gained through this K01 award, Dr. Owora plans
to submit a R01 grant to evaluate the efficacy of the PDM for improved early detection of childhood asthma.
项目总结/文摘
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Arthur Hamie Owora其他文献
External validation and update of the pediatric asthma risk score as a passive digital marker for childhood asthma using integrated electronic health records
利用综合电子健康记录对儿科哮喘风险评分作为儿童哮喘的被动数字标志物进行外部验证和更新
- DOI:
10.1016/j.eclinm.2025.103254 - 发表时间:
2025-06-01 - 期刊:
- 影响因子:10.000
- 作者:
Arthur Hamie Owora;Bowen Jiang;Yash Shah;Benjamin Gaston;Malaz Boustani - 通讯作者:
Malaz Boustani
Arthur Hamie Owora的其他文献
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{{ truncateString('Arthur Hamie Owora', 18)}}的其他基金
Developing a Passive Digital Marker for the Prediction of Childhood Asthma Treatment Response
开发用于预测儿童哮喘治疗反应的被动数字标记
- 批准号:
10511534 - 财政年份:2022
- 资助金额:
$ 16.2万 - 项目类别:
Developing a Passive Digital Marker for the Prediction of Childhood Asthma Treatment Response
开发用于预测儿童哮喘治疗反应的被动数字标记
- 批准号:
10670853 - 财政年份:2022
- 资助金额:
$ 16.2万 - 项目类别:
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