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: 10039613
• 负责人: Joo Heung Yoon
• 金额: $ 18.99万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10039613
• 关键词: Acute; Adverse event; Algorithms; Artificial Intelligence; Award; Bioinformatics; Blood Donations; Blood Pressure; Blood donor; Blood flow; Cardiovascular system; Characteristics; Clinical; Clinical Data; Clinical Informatics; Clinical Research; Clinical Trials; Complex; Computerized Medical Record; Computers; Critical Care; Critical Illness; Data; Data Analyses; Data Science; Data Set; Databases; Detection; Development; Distress; Early Diagnosis; Early identification; Electrocardiogram; Electroconvulsive Therapy; Ensure; Environment; Evolution; Failure; Family suidae; Financial compensation; Goals; Gold; Grant; Health Care Costs; Hemoglobin concentration result; Hemorrhage; Heterogeneity; Hypersensitivity; Hypotension; Hypovolemia; Hypovolemics; Individual; Information Sciences; Intensive Care Units; Interdisciplinary Study; K-Series Research Career Programs; Learning; Life; Link; Liquid substance; Lung; Machine Learning; Master of Science; Measures; Medicine; Mentors; Metabolic; Modeling; Morbidity - disease rate; Nature; Organ; Outcome; Oxygen; Patients; Pattern; Phenotype; Photoplethysmography; Physicians; Physiological; Physiology; Plethysmography; Predictive Analytics; Probability; Psychological reinforcement; Reaction; Reporting; Research; Research Methodology; Resuscitation; Risk; Savings; Scientist; Series; Severities; Severity of illness; Shock; Tachycardia; Therapeutic; Time; Training; Treatment Protocols; Universities; Vasoconstrictor Agents; Work; adverse event risk; adverse outcome; alternative treatment; analog; base; biomedical informatics; career; causal model; clinically actionable; clinically relevant; cohort; computer science; computerized data processing; data acquisition; deep learning; density; hemodynamics; high risk; human subject; improved; improved outcome; individualized medicine; machine learning algorithm; mortality; neurotensin mimic 2; outcome forecast; personalized diagnostics; personalized medicine; personalized predictions; predictive modeling; response; signal processing; skills; supervised learning; treatment strategy; unsupervised learning; volunteer

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.

K23摘要 此应用程序是K23指导临床科学家研究职业发展奖 开发机器学习驱动的预测模型和治疗策略 重症患者的循环性休克”。 我是匹兹堡大学的一名肺病和重症监护医生。该奖项将促进 我在临床研究方法、临床信息学和计算机方面的高级培训 科学发展我的职业生涯作为一个物理学家，科学家专注于数据驱动的动态研究 危重病人的生理学该提案的主要目的是发展个性化的 重症患者休克的预测模型和治疗策略。 本研究的目的是： 1)建立基于机器学习的心动过速和低血压的预测模型 在健康献血志愿者中使用非侵入性波形数据进行献血，并创建基线 与循环性休克比较的特征 2)提供一个可操作的定义，预测模型，区分生理进化 对休克的认识，以及ICU患者循环休克的个性化治疗。 通过这个建议，我将发展在机器学习，临床生物信息学， 临床研究我将完成生物医学信息学硕士学位，学习先进的数据- 以研究方法为导向，加强我的技术培训。这个奖项将是关键的一步 朝着我的长期目标，成为一名独立的医生科学家， 通过临床试验进行重症监护医学分析。我的导师迈克尔·平斯基博士 （生理学、功能性血液动力学）和Gilles Clermont博士（重症监护、算法、数据科学） 他将确保我提出的目标的成功实现。我的指导委员会还包括一个 顾问，Milos Hauskrecht博士-计算机科学和信息领域的著名计算机科学家 匹兹堡大学的科学系。我的工作将在肺科完成， 匹兹堡大学的过敏和重症监护医学， 致力于培养医学科学家