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: 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/10673670
• 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; Compensation; 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; Goals; 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; Prognosis; Psychological reinforcement; Reaction; Reporting; Research; Research Methodology; Resuscitation; Risk; 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; 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; machine learning model; machine learning prediction; mortality; neurotensin mimic 2; personalized diagnostics; personalized medicine; personalized predictions; predictive modeling; progression risk; 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 本申请表是K23临床科学家指导研究职业发展奖的申请书 题为《开发机器学习驱动的预测模型和治疗策略》 用于危重病人的循环系统休克“。 我是匹兹堡大学的一名肺部和重症监护内科医生。这项裁决将有助于 我获得了临床研究方法、临床信息学和计算机方面的高级培训 科学将发展我作为一名内科医生的职业生涯-科学家专注于数据驱动的动态研究 危重病人的生理学。这项提议的主要目标是开发个性化的 危重病人休克的预测模型及救治策略。 这项研究的目的是： 1)建立了基于机器学习的心动过速和低血压预测模型。 在健康献血者志愿者中使用无创波形数据进行献血，并创建基线 与循环性休克的特征比较 2)提供可操作的定义、预测模型、生理进化的区分 走向休克，对ICU患者循环性休克的个体化治疗。 通过这项提议，我将发展机器学习、临床生物信息学和 临床研究。我将完成生物医学信息学的理学硕士学位，以学习高级数据- 推动研究方法，加强我的技术培训。这一奖项将是关键的一步 朝着我的长期目标前进，成为一名独立的内科科学家，拥有预测方面的专业知识 通过临床试验对重症监护医学进行分析。我已经委托迈克尔·平斯基博士 (生理学、功能血流动力学)和Gilles Clermont博士(重症监护、算法、数据科学) 世卫组织将确保成功完成我提出的目标。我的指导委员会还包括一个 顾问，Milos Hauskrecht博士--计算机科学和信息学领域的著名计算机科学家 匹兹堡大学理科专业。我的工作将在肺科内完成， 匹兹堡大学的过敏和重症护理医学，有着广泛的记录 致力于发展内科科学家。

项目成果

Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact

• DOI: 10.48550/arxiv.2206.09074
• 发表时间: 2022-06
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Arnab Dey-;Mononito Goswami;J. H. Yoon;G. Clermont;M. Pinsky;M. Hravnak;A. Dubrawski

Current challenges in adopting machine learning to critical care and emergency medicine.

• 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