权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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：
Arthur Hamie Owora
金额：
$ 16.2万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
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：
https://reporter.nih.gov/project-details/10571461
关键词：
Active Learning Address Adult Age Algorithms Asthma Award Biometry Birth Blinded Blood Tests Child Childhood Asthma Clinical Clinical Research Data Detection Development Diagnosis Digital biomarker Disease Early Diagnosis Early treatment Electronic Health Record Enrollment Epidemiologist Epidemiology Evaluation Evaluation Studies Fellowship Future Goals Grant Health Indiana Individual Intervention Intuition Knowledge Logistic Regressions Machine Learning Medical Medical History Mentored Research Scientist Development Award Mentors Modeling Morbidity - disease rate Mothers National Heart, Lung, and Blood Institute Natural History Nursery Schools Online Systems Pathway interactions Patient Care Patient-Focused Outcomes Performance Phenotype Physicians Postdoctoral Fellow Predictive Analytics Prognosis Prognostic Factor Proxy Random Allocation Randomized Research Risk Sample Size School-Age Population Scientist Spirometry Symptoms Testing Time Training Training Activity Translating United States United States National Institutes of Health Validation career clinical decision support clinical decision-making clinical practice cohort design digital disorder risk effectiveness testing efficacious intervention efficacy evaluation experience health information technology implementation research improved insight machine learning algorithm machine learning method multidisciplinary novel pediatrician personalized care personalized medicine point of care primary caregiver prognostic prognostic signature randomized, clinical trials skills support tools tool translational scientist treatment choice usability

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