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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
开发机器学习驱动的危重患者循环休克预测模型和治疗策略
批准号:
10673670
负责人:
Joo Heung Yoon
金额:
$18.61万
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 TrialsCompensationComplexComputerized Medical RecordComputersCritical CareCritical IllnessDataData AnalysesData ScienceData SetDatabasesDetectionDevelopmentDistressEarly DiagnosisEarly identificationElectrocardiogramElectroconvulsive TherapyEnsureEnvironmentEvolutionFailureFamily suidaeGoalsGrantHealth Care CostsHemoglobin concentration resultHemorrhageHeterogeneityHypersensitivityHypotensionHypovolemiaHypovolemicsIndividualInformation SciencesIntensive Care UnitsInterdisciplinary StudyK-Series Research Career ProgramsLearningLifeLinkLiquid substanceLungMachine LearningMaster of ScienceMeasuresMedicineMentorsMetabolicModelingMorbidity - disease rateNatureOrganOutcomeOxygenPatientsPatternPhenotypePhotoplethysmographyPhysiciansPhysiologicalPhysiologyPlethysmographyPredictive AnalyticsProbabilityPrognosisPsychological reinforcementReactionReportingResearchResearch MethodologyResuscitationRiskScientistSeriesSeveritiesSeverity of illnessShockTachycardiaTherapeuticTimeTrainingTreatment ProtocolsUniversitiesVasoconstrictor AgentsWorkadverse event riskadverse outcomealternative treatmentanalogbiomedical informaticscareercausal modelclinically actionableclinically relevantcohortcomputer sciencecomputerized data processingdata acquisitiondeep learningdensityhemodynamicshigh riskhuman subjectimprovedimproved outcomeindividualized medicinemachine learning algorithmmachine learning modelmachine learning predictionmortalityneurotensin mimic 2personalized diagnosticspersonalized medicinepersonalized predictionspredictive modelingprogression riskresponsesignal processingskillssupervised learningtreatment strategyunsupervised learningvolunteer

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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(2)
专著(0)
科研奖励(0)
会议论文
DOI: 10.48550/arxiv.2206.09074
发表时间: 2022-06
期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子: --
作者: [Arnab Dey-;Mononito Goswami;J. H. Yoon;G. Clermont;M. Pinsky;M. Hravnak;A. Dubrawski]
通讯作者: Arnab Dey-;Mononito Goswami;J. H. Yoon;G. Clermont;M. Pinsky;M. Hravnak;A. Dubrawski
DOI: 10.15441/ceem.23.041
发表时间: 2023-06
期刊: CLINICAL AND EXPERIMENTAL EMERGENCY MEDICINE
影响因子: 1.9
作者: [Kang, Cyra-Yoonsun, Yoon, Joo Heung]
通讯作者: Yoon, Joo Heung
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
Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
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