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
基本信息
- 批准号:10470098
- 负责人:
- 金额:$ 16.63万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:37 weeks gestationAccountingAcute DiseaseAddressAdverse eventAffectAreaAwardBig DataBioinformaticsBiometryBirthBirth WeightBostonBronchopulmonary DysplasiaCardiacChronicClinicalClinical DataClinical MedicineCollaborationsComputational TechniqueConceptionsDataData AnalysesData ScientistData SourcesDatabasesDiagnosisDoctor of PhilosophyEducationElectronic Health RecordEnvironmentEventFutureGestational AgeGoalsGraduate DegreeGrantHealthHealthcareHealthcare SystemsHeartHeart DiseasesHospitalsHuman PathologyIncidenceInfantInformaticsInstitutional Review BoardsInsurance CarriersLifeLiquid substanceLungLung diseasesMachine LearningMeasuresMentorsMentorshipMethodologyModelingMonitorMorbidity - disease rateNatureNecrotizing EnterocolitisNeonatalNeonatal Intensive Care UnitsNeonatologyOutcomePatent Ductus ArteriosusPatientsPatternPediatricsPerformancePerinatalPhysiciansPhysiologicalPopulationPregnancyPregnant WomenPremature BirthPremature InfantPremature LaborReproducibilityResearchResearch PersonnelResearch SupportRestRetinopathy of PrematurityRiskRisk EstimateScientistSepsisSideSignal TransductionSourceTeaching HospitalsTechniquesTimeTrainingTranslatingUpdateVulnerable PopulationsWorkclinical practiceclinical predictorsclinical prognosticdata resourcedeep learningdeep learning algorithmdeep learning modeldesigneducation planningelectronic dataexperienceimprovedinsurance claimsmortalitypeerportabilityprediction algorithmpredictive modelingprematurerespiratory distress syndromerisk predictionsocialstatistical and machine learningstatisticsstructured data
项目摘要
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.
研究综述
该奖项的目标是为安德鲁·比姆博士提供研究支持和全面的指导
旨在将他转变为围产期和新生儿信息学的独立调查员。早产
(PTL)是在怀孕37周之前发生的分娩,伴随着巨大的健康和经济
后果。到目前为止,早产儿的肺部和心脏发病率是最高的。
针对这些重要成果的机器学习技术仍处于开发阶段。研究策略是
利用现有的大量资源,重点开发两个非常重要的临床场景的预测模型
医疗保健数据。《特定目标1》的重点是开发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2的重点是调查深度学习的使用
用于预测NICU中早产儿的临床轨迹。目前,管理和预期
这两种临床情景都是具有挑战性的,我们预测能力的进步可能会显著提高
医疗保健系统的质量和效率。这些模型将使用现有的50个数据库构建
通过与美国一家主要健康保险公司的合作,获得了100万名患者的生命。具体目标3旨在
了解使用此唯一数据资源构建的模型如何转换和概括为来自
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的关键担忧。
教育计划的重点是增加比姆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。这项额外的教育将使比姆博士获得
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
未来与临床医生的合作。比姆博士导师委员会的组成,
包括新生儿学、生物统计学和翻译信息学方面的专业知识,反映了他长期希望成为
定量科学家,与执业医生并肩工作,以便将定量研究转化为
转化为有效的临床实践。
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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Andrew L. Beam其他文献
Correction to: Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review
- DOI:
10.1007/s40264-023-01273-9 - 发表时间:
2023-02-24 - 期刊:
- 影响因子:3.800
- 作者:
Benjamin Kompa;Joe B. Hakim;Anil Palepu;Kathryn Grace Kompa;Michael Smith;Paul A. Bain;Stephen Woloszynek;Jeffery L. Painter;Andrew Bate;Andrew L. Beam - 通讯作者:
Andrew L. Beam
534 Regional differences in utilization of 17α-hydroxyprogesterone caproate (17OHP)
- DOI:
10.1016/j.ajog.2020.12.555 - 发表时间:
2021-02-01 - 期刊:
- 影响因子:
- 作者:
Jessica M. Hart;Joe B. Hakim;Blair J. Wylie;Andrew L. Beam - 通讯作者:
Andrew L. Beam
Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy
通过体内共聚焦显微镜诊断神经性角膜疼痛的深度学习模型的开发与验证
- DOI:
10.1038/s41746-025-01577-3 - 发表时间:
2025-05-14 - 期刊:
- 影响因子:15.100
- 作者:
Neslihan Dilruba Koseoglu;Eric Chen;Rudraksh Tuwani;Benjamin Kompa;Stephanie M. Cox;M. Cuneyt Ozmen;Mina Massaro-Giordano;Andrew L. Beam;Pedram Hamrah - 通讯作者:
Pedram Hamrah
TIER: Text-Image Entropy Regularization for CLIP-style models
TIER:CLIP 样式模型的文本图像熵正则化
- DOI:
10.48550/arxiv.2212.06710 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Anil Palepu;Andrew L. Beam - 通讯作者:
Andrew L. Beam
Andrew L. Beam的其他文献
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{{ truncateString('Andrew L. Beam', 18)}}的其他基金
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 - 财政年份:2019
- 资助金额:
$ 16.63万 - 项目类别:
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 - 财政年份:2019
- 资助金额:
$ 16.63万 - 项目类别:
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:
9928552 - 财政年份:2019
- 资助金额:
$ 16.63万 - 项目类别:
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