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: 10646498
• 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/10646498
• 关键词: 37 weeks gestation; Accounting; Acute; Address; Adverse event; Affect; Area; Award; Big Data; Bioinformatics; Biometry; Birth; Birth Weight; Boston; Bronchopulmonary Dysplasia; Cardiac; Categories; Chronic; Clinical; Clinical Data; Clinical Medicine; Collaborations; Computational Technique; Conceptions; Data; Data Analyses; Data Scientist; Data Sources; Databases; Decision Making; 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 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; deep learning model; design; education planning; electronic health data; experience; improved; insurance claims; model building; mortality; peer; portability; prediction algorithm; predictive modeling; premature; prognostic; respiratory distress syndrome; risk prediction; risk prediction model; social; 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.

研究概要 该奖项的目标是为安德鲁·比姆博士提供研究支持和全面指导 旨在将他转变为围产期和新生儿早产信息学的独立研究者。 (PTL) 是指在妊娠 37 周之前发生的分娩，伴随着巨大的健康和经济损失 然而，早产儿的肺部和心脏发病率最高。 这些重要成果的机器学习技术尚未开发。 重点利用现有的大量资源为两个非常重要的临床场景开发预测模型 具体目标 1 的重点是开发一种新形式的机器学习，称为深度学习。 用于预测孕妇的 PTL，而特定目标 2 的重点是研究深度学习的使用 用于预测 NICU 中早产儿的临床轨迹 目前，管理和预测。 这两种临床情况都具有挑战性，我们的预测能力的进步可以显着提高 这些模型将使用现有的 50 个数据库来构建。 具体目标 3 旨在通过与美国一家主要健康保险公司合作获得 100 万患者的生命。 了解使用这种独特的数据资源构建的模型如何转换并概括为来自 波士顿地区医院的电子健康记录，这是所有医疗数据科学家关注的重点。 该教育计划的重点是通过以下方式增强 Beam 博士在统计学和生物信息学方面的研究生学位： 临床医学和人类病理学方面的额外培训将授予 Beam 博士资格。 更深入地了解这些人群面临的临床问题，并将允许更多的液体 Beam 博士的导师委员会的组成，其中 包括新生儿学、生物统计学和转化信息学方面的专业知识，反映了他长期渴望成为 与执业医师并肩工作的定量科学家，以便转化定量研究 转化为有影响力的临床实践。

项目成果

Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008-2016.

商业保险人群中早产儿和低出生体重儿的医疗保健支出估计：2008-2016 年。

• 发表时间: 2020
• 作者: Beam, Andrew L;Fried, Inbar;Palmer, Nathan;Agniel, Denis;Brat, Gabriel;Fox, Kathe;Kohane, Isaac;Sinaiko, Anna;Zupancic, John A F;Armstrong, Joanne
• 通讯作者: Armstrong, Joanne

Precision global health for real-time action.

精准的全球健康实时行动。

• 发表时间: 2020
• 作者: Flahault, Antoine;Utzinger, Jürg;Eckerle, Isabella;Sheath, Danny J;de Castañeda, Rafael Ruiz;Bolon, Isabelle;Bempong, Nefti;Andayi, Fred
• 通讯作者: Andayi, Fred

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension.

涉及人工智能干预措施的临床试验报告的报告指南：CONSORT-AI 扩展。

• 发表时间: 2020-09-09
• 作者: Liu, Xiaoxuan;Rivera, Samantha Cruz;Moher, David;Calvert, Melanie J;Denniston, Alastair K;SPIRIT
• 通讯作者: SPIRIT

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI].

[涉及人工智能干预措施的临床试验方案指南：SPIRIT-AI 扩展Diretrizes para protocolos de ensaios clínicos com intervenães que utilizam inteligáncia Artificial Artificial Intelligence: a extensão SPIRIT-AI]。

• 发表时间: 2023
• 作者: Cruz Rivera, Samantha;Liu, Xiaoxuan;Chan, An;Denniston, Alastair K;Calvert, Melanie J;Grupo de Trabajo SPIRIT;Grupo Directivo SPIRIT;Grupo de Consenso SPIRIT
• 通讯作者: Grupo de Consenso SPIRIT

Artificial Intelligence in Medicine.

医学中的人工智能。

• 发表时间: 2023-03-30
• 作者: Beam, Andrew L;Drazen, Jeffrey M;Kohane, Isaac S;Leong, Tze;Manrai, Arjun K;Rubin, Eric J
• 通讯作者: Rubin, Eric J