Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction
基于深度学习的射血分数保留的心力衰竭表型分析和治疗优化
基本信息
- 批准号:10592341
- 负责人:
- 金额:$ 58.35万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-03-15 至 2026-02-28
- 项目状态:未结题
- 来源:
- 关键词:3-DimensionalAortic Valve StenosisBig DataBiological MarkersCardiacChronic Obstructive Pulmonary DiseaseClinicalClinical DataClinical TrialsCodeCommunitiesComplexComputer softwareDataDevelopmentDiabetes MellitusDiagnosisDiagnosticDimensionsDiseaseDisparateEFRACEconomic BurdenElectronic Health RecordEnvironmental Risk FactorFailureGoalsHeartHeart failureHospitalizationHypertensionImageImage AnalysisKnowledgeLaboratoriesLearningLife StyleMachine LearningMagnetic ResonanceMalignant NeoplasmsMeasurementMethodsModelingMorbidity - disease rateMorphologyNatureObesity EpidemicOrganOutcomePatient imagingPatientsPerformancePharmaceutical PreparationsPhenotypePhysiciansPilot ProjectsPopulationPrevalenceProceduresPrognosisPsychological reinforcementPublic HealthPublishingQuality of lifeRadiology SpecialtyRecommendationRecording of previous eventsReportingResearchResourcesSepsisSeriesSource CodeSurfaceSymptomsSyndromeTechniquesTherapeuticTreatment EfficacyValidationWorkaging populationautomated analysisbiomarker identificationcardiac magnetic resonance imagingclinical decision supportclinical efficacyclinical investigationclinically significantcomorbiditycomputerized data processingdeep learningdeep reinforcement learningeffective therapyelectronic health informationfeature extractionheart imagingimage processingimaging biomarkerimprovedindividualized medicinelifestyle factorslongitudinal analysismagnetic resonance imaging biomarkermortalitymultimodalitynovelnovel therapeuticsopen dataoptimal treatmentspersonalized medicinepreservationreconstructionrecurrent neural networkrisk stratificationshape analysissymposiumtargeted treatmenttreatment optimizationtreatment planningtreatment strategytrend
项目摘要
Heart failure with preserved ejection fraction (HFpEF) is a major public health problem
that is rising in prevalence with the aging population and the epidemics of obesity, diabetes, and
hypertension. HFpEF accounts for around 50% of all heart failure (HF) cases with a prevalence
of at least 3 million in the U.S. HFpEF is associated with high morbidity and mortality. After HF
hospitalization, the 5-year survival of HFpEF is a dismal 35%, which is worse than most cancers.
In addition, quality of life in HFpEF is as poor or worse than HF with reduced ejection fraction
(HFrEF). A series of large-scale clinical trials has been conducted, but most of them only provided
neutral result and failed to prove the efficacy of treatments. The alarming trend of HFpEF with
lack of effective therapies for patients constitutes a major public health problem.
Recent studies have attributed this failure to distinct systemic nature of HFpEF syndrome
and proposing sub-phenotypes within the heterogeneous HFpEF syndrome, which highlighted
the increasing need for better-targeted therapies to specific HFpEF subtypes. The seemingly
disparate but complex interrelated phenotypes, along with comorbidities, lifestyle and
environmental factors, make the multi-organ syndrome best beneficial from a big data approach.
However, conventional studies usually only included limited cross-sectional clinical symptoms,
lab results and/or gross measurements on cardiac imaging to investigate HFpEF, overlooking the
rich temporal information from electronic health record (EHR) and detailed spatial information
reserved in imaging.
In this proposal, we will introduce advance shape analysis method to extract novel image
features and biomarker from CMR images and validate at population level (Aim 1). We will then
combine image information with multi-dimensional temporal EHR data to jointly identify clinically
significant HFpEF subclasses (i.e. phenotyping) using state-of-art machine learning technique
(Aim 2). Towards therapeutic goals based on phenotyping, we will further investigate optimal
treatment strategies with current available agents using deep reinforcement learning (RL) based
on massive EHR data to meet the pressing need before ongoing trials provide sufficient evidence
on new drugs with proved clinical efficacy (Aim 3). Furthermore, we will develop an online, open-
access platform to facilitating the sharing of code, data and knowledge of this study (Aim 4). We
believe this research can improve our understanding, phenotyping and management of HFpEF,
which might positively ease the clinical and economic burdens in turn both in U.S. and worldwide.
心力衰竭伴保留射血分数(HFpEF)是一个重大的公共卫生问题
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
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Quanzheng Li其他文献
Quanzheng Li的其他文献
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{{ truncateString('Quanzheng Li', 18)}}的其他基金
Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction
基于深度学习的射血分数保留的心力衰竭表型分析和治疗优化
- 批准号:
10444412 - 财政年份:2022
- 资助金额:
$ 58.35万 - 项目类别:
TR&D2: Advanced Statistical Image Reconstruction & Physics Informed Artificial Intelligence for Quantitative PET/MR
TR
- 批准号:
10651773 - 财政年份:2017
- 资助金额:
$ 58.35万 - 项目类别:
Unified Joint Statistical Reconstruction of PET & MR
PET统一联合统计重建
- 批准号:
10263164 - 财政年份:2017
- 资助金额:
$ 58.35万 - 项目类别:
Superhigh Sensitivity SPECT Imaging with Dense Camera Arrays
使用密集相机阵列进行超高灵敏度 SPECT 成像
- 批准号:
8702789 - 财政年份:2014
- 资助金额:
$ 58.35万 - 项目类别:
Superhigh Sensitivity SPECT Imaging with Dense Camera Arrays
使用密集相机阵列进行超高灵敏度 SPECT 成像
- 批准号:
8814222 - 财政年份:2014
- 资助金额:
$ 58.35万 - 项目类别:
Quantitative Methods for Clinical Whole Body Dynamic PET
临床全身动态 PET 的定量方法
- 批准号:
8237421 - 财政年份:2011
- 资助金额:
$ 58.35万 - 项目类别:
Quantitative Methods for Clinical Whole Body Dynamic PET
临床全身动态 PET 的定量方法
- 批准号:
8588924 - 财政年份:2011
- 资助金额:
$ 58.35万 - 项目类别:
Quantitative Methods for Clinical Whole Body Dynamic PET
临床全身动态 PET 的定量方法
- 批准号:
8399088 - 财政年份:2011
- 资助金额:
$ 58.35万 - 项目类别:
An Integrated Statistical Framework for Lesion Detection Using Dynamic PET
使用动态 PET 进行病变检测的综合统计框架
- 批准号:
8421579 - 财政年份:2010
- 资助金额:
$ 58.35万 - 项目类别:
An Integrated Statistical Framework for Lesion Detection Using Dynamic PET
使用动态 PET 进行病变检测的综合统计框架
- 批准号:
7877521 - 财政年份:2010
- 资助金额:
$ 58.35万 - 项目类别:
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