Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
批准号:
10039613
负责人:
Joo Heung Yoon
金额:
$18.99万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-08-01 至 2025-07-31
关键词:
AcuteAdverse eventAlgorithmsArtificial IntelligenceAwardBioinformaticsBlood DonationsBlood PressureBlood donorBlood flowCardiovascular systemCharacteristicsClinicalClinical DataClinical InformaticsClinical ResearchClinical TrialsComplexComputerized Medical RecordComputersCritical CareCritical IllnessDataData AnalysesData ScienceData SetDatabasesDetectionDevelopmentDistressEarly DiagnosisEarly identificationElectrocardiogramElectroconvulsive TherapyEnsureEnvironmentEvolutionFailureFamily suidaeFinancial compensationGoalsGoldGrantHealth Care CostsHemoglobin concentration resultHemorrhageHeterogeneityHypersensitivityHypotensionHypovolemiaHypovolemicsIndividualInformation SciencesIntensive Care UnitsInterdisciplinary StudyK-Series Research Career ProgramsLearningLifeLinkLiquid substanceLungMachine LearningMaster of ScienceMeasuresMedicineMentorsMetabolicModelingMorbidity - disease rateNatureOrganOutcomeOxygenPatientsPatternPhenotypePhotoplethysmographyPhysiciansPhysiologicalPhysiologyPlethysmographyPredictive AnalyticsProbabilityPsychological reinforcementReactionReportingResearchResearch MethodologyResuscitationRiskSavingsScientistSeriesSeveritiesSeverity of illnessShockTachycardiaTherapeuticTimeTrainingTreatment ProtocolsUniversitiesVasoconstrictor AgentsWorkadverse event riskadverse outcomealternative treatmentanalogbasebiomedical informaticscareercausal modelclinically actionableclinically relevantcohortcomputer sciencecomputerized data processingdata acquisitiondeep learningdensityhemodynamicshigh riskhuman subjectimprovedimproved outcomeindividualized medicinemachine learning algorithmmortalityneurotensin mimic 2outcome forecastpersonalized diagnosticspersonalized medicinepersonalized predictionspredictive modelingresponsesignal processingskillssupervised learningtreatment strategyunsupervised learningvolunteer
中文摘要
点击翻译按钮获取中文摘要
英文摘要
K23 Abstract
This application is for a K23 Mentored Clinical Scientist Research Career Development Award
entitled “Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies
for Circulatory Shock in Critically-ill Patients”.
I am a pulmonary and critical care physician at the University of Pittsburgh. This award will facilitate
my acquisition of advanced training in clinical research methods, clinical informatics, and computer
science to develop my career as a physician-scientist focused on data-driven studies of dynamic
physiology in critically-ill patients. The main objective of this proposal is to develop individualized
prediction models and treatment strategies for shock among critically-ill patients.
The aims of this study are:
1) To build machine learning-based prediction models of tachycardia and hypotension following blood
donation using non-invasive waveform data in healthy blood donor volunteers, and create baseline
features to compare with circulatory shock
2) To provide an operational definition, prediction models, differentiation of physiologic evolution
towards shock, and personalized treatment of circulatory shock in ICU patients.
Through this proposal, I will develop advanced skills in machine-learning, clinical bioinformatics, and
clinical research. I will complete a Master of Science in Biomedical Informatics to learn advanced data-
driven research methodologies to strengthen my technical training. This award will be a critical step
towards my long-term goal, being an independent physician scientist, with expertise in prediction
analytics in critical care medicine through clinical trials. I have committed mentors Dr. Michael Pinsky
(physiology, functional hemodynamics) and Dr. Gilles Clermont (critical care, algorithms, data science)
who will ensure successful completion of my proposed aims. My mentoring committee also includes an
advisor, Dr. Milos Hauskrecht - a renowned computer scientist in the Computer Science and Information
Sciences at the University of Pittsburgh. My work will be completed within the Division of Pulmonary,
Allergy, and Critical Care Medicine at the University of Pittsburgh, which has an extensive track record
of committing to the development of physician scientists.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
-
批准号:10673670
-
项目类别:
-
资助金额:$18.61万
-
财政年份:2020
-
负责人:Joo Heung Yoon
-
依托单位:
Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
-
批准号:10221741
-
项目类别:
-
资助金额:$18.96万
-
财政年份:2020
-
负责人:Joo Heung Yoon
-
依托单位:
Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
-
批准号:10450107
-
项目类别:
-
资助金额:$18.82万
-
财政年份:2020
-
负责人:Joo Heung Yoon
-
依托单位:
海外基金