Predicting Pulmonary and Cardiac Morbidity in Preterm Infants with Deep Learning

通过深度学习预测早产儿的肺部和心脏发病率

• 批准号: 10198019
• 负责人: Andrew L. Beam
• 金额: $ 16.63万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198019
• 关键词: 37 weeks gestation; Accounting; Acute Disease; Address; Adverse event; Affect; Area; Award; Big Data; Bioinformatics; Biometry; Birth; Birth Weight; Boston; Bronchopulmonary Dysplasia; Cardiac; Chronic; Clinical; Clinical Data; Clinical Medicine; Collaborations; Computational Technique; Conceptions; Data; Data Analyses; Data Scientist; Data Sources; Databases; Diagnosis; Doctor of Philosophy; Education; Electronic Health Record; Environment; Event; Future; Gestational Age; Goals; Graduate Degree; Grant; Health; Healthcare; Healthcare Systems; Heart; Heart Diseases; Hospitals; Human Pathology; Incidence; Infant; Informatics; Institutional Review Boards; Insurance Carriers; Life; Liquid substance; Lung; Lung diseases; Machine Learning; Measures; Mentors; Mentorship; Methodology; Modeling; Monitor; Morbidity - disease rate; Nature; Necrotizing Enterocolitis; Neonatal; Neonatal Intensive Care Units; Neonatology; Outcome; Patent Ductus Arteriosus; Patients; Pattern; Pediatrics; Performance; Perinatal; Physicians; Physiological; Population; Pregnancy; Pregnant Women; Premature Birth; Premature Infant; Premature Labor; Reproducibility; Research; Research Personnel; Research Support; Rest; Retinopathy of Prematurity; Risk; Risk Estimate; Scientist; Sepsis; Side; Signal Transduction; Source; Teaching Hospitals; Techniques; Time; Training; Translating; Update; Vulnerable Populations; Work; clinical practice; clinical predictors; data resource; deep learning; deep learning algorithm; design; education planning; electronic data; experience; improved; insurance claims; mortality; peer; portability; prediction algorithm; predictive modeling; premature; prognostic; respiratory distress syndrome; risk prediction; social; statistical and machine learning; statistics; structured data

RESEARCH SUMMARY The goal of this award is to provide Andrew Beam, PhD with research support and comprehensive mentoring designed to transition him to an independent investigator in perinatal and neonatal informatics. Preterm labor (PTL) is labor which occurs before 37 weeks of gestation and carries with it enormous health and financial consequences. Preterm infants have some of the highest levels of pulmonary and cardiac morbidity, yet machine-learning techniques for these important outcomes remains under developed. The research strategy is focused developing predictive models for two very important clinical scenarios using large sources of existing healthcare data. The focus of Specific Aim 1 develops a new form of machine learning known as deep learning for predicting PTL in pregnant women, while the focus of Specific Aim 2 investigates the use of deep learning for predicting clinical trajectories of preterm infants in the NICU. Currently, management and anticipation of both clinical scenarios is challenging and advancement in our predictive capacity could dramatically improve the quality and efficiency of the healthcare system. These models will be built using an existing database of 50 million patient-lives obtained through a partnership with a major US health insurer. Specific Aim 3 seeks to understand how the models constructed using this unique data resource translate and generalize to data from the electronic health records of Boston-area hospitals, which is a key concern for all healthcare data scientists. The education plan focuses on augmenting Dr. Beam’s graduate degrees in statistics and bioinformatics with additional training in clinical medicine and human pathology. This additional education will grant Dr. Beam a deeper understanding of the clinical problems faced by these populations and will allow for more fluid collaborations with clinicians in the future. The composition of Dr. Beam’s mentorship committee, which includes expertise in neonatology, biostatistics, and translational informatics, reflects his long-term desire to be quantitative scientist who works side-by-side practicing physicians so that quantitative research is translated into impactful clinical practice.

研究摘要 该奖项的目的是为Andrew Beam，PhD提供研究支持和全面的心理 旨在将他过渡到围产期和新生儿信息的独立调查员。早产 （PTL）是在妊娠37周之前发生的劳动 结果。早产儿具有最高水平的肺和心脏发病率 这些重要结果的机器学习技术仍在开发中。研究策略是 专注于使用现有的大量来源的两个非常重要的临床场景开发预测模型 医疗保健数据。特定目标1的重点1开发了一种新的机器学习形式，称为深度学习 用于预测孕妇的PTL，而特定目标的重点2调查了深度学习的使用 用于预测NICU早产儿的临床轨迹。目前，管理和期待 两种临床方案都具有挑战性，并且在我们的预测能力方面的进步可能会大大改善 医疗保健系统的质量和效率。这些模型将使用50的现有数据库构建 通过与美国主要健康保险公司建立合作伙伴关系获得的百万个患者生活。特定目标3试图 了解使用此独特数据资源构建的模型如何转化并推广到来自 波士顿地区医院的电子健康记录，这是所有医疗保健数据科学家的关键问题。 该教育计划的重点是增强Beam博士在统计和生物信息学领域的研究生学位 临床医学和人类病理学的其他培训。这种额外的教育将授予Beam博士 对这些人群所面临的临床问题的更深入了解，将允许更多的流动性 将来与临床医生的合作。 Beam博士的Mentalship委员会的组成，该委员会 包括新生儿学，生物统计学和翻译信息的专业知识，反映了他的长期愿望 并排工作医生的定量科学家，以便翻译定量研究 进入有影响力的临床实践。