Improving antibiotic treatment decisions through machine learning

通过机器学习改善抗生素治疗决策

• 批准号: 10443744
• 负责人: Sanjat Kanjilal
• 金额: $ 14.92万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-02 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10443744
• 关键词: Improving; antibiotic; treatment; decisions; through

PROJECT SUMMARY / ABSTRACT Infections from antibiotic resistant bacteria represent one of the biggest challenges facing modern medical care. Suboptimal antibiotic use is one of the major drivers for antibiotic resistance, however clinicians lack robust tools to help them make rational treatment decisions at the patient-level. The goal of this 3-year mentored clinical scientist research career development program is to apply state-of-the-art machine learning models to routinely collected data in the electronic health record to predict the risk of antimicrobial resistance (AMR) prior to, and after antibiotic treatment. The candidate, Dr. Sanjat Kanjilal, has identified two important clinical problems where improved risk prediction for AMR could have a significant impact on quality of care. The first is the overuse of broad-spectrum antibiotics in patients presenting with community-onset sepsis. To address this, the candidate will develop a set of machine learning prediction models trained on routinely collected data in the electronic health record to help clinicians identify which antibiotic(s) will effectively treat the patient's pathogen while being of the narrowest possible spectrum. The second problem is the inability to assess the risk of a patient developing an antibiotic resistant infection after being treated with an antibiotic. The candidate proposes to build a robust causal inference model using targeted maximum likelihood estimation combined with machine learning to estimate the impact of taking various commonly used outpatient antibiotics on the risk of developing a drug resistant infection in the 12 month period after treatment. The results of this work will form the basis of a precision medicine approach to antibiotic stewardship and treatment. The candidate is a practicing infectious diseases clinician and the Associate Medical Director of the clinical microbiology laboratory at the Brigham & Women's Hospital. He has prior experience in building machine learning algorithms that provide robust antimicrobial stewardship. His unique background combined with the rich supporting environment of the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute, position him well for the transition to becoming an independently funded clinician- scientist working at the interface of infectious diseases and machine learning. He has assembled a multidisciplinary mentorship team consisting of experts in sepsis epidemiology, antimicrobial stewardship, implementation science, machine learning and causal inference to help him achieve his goals and has identified a comprehensive training plan that provides him the skills necessary to become a leader in his field. His short term goal is to become an expert in the development of machine learning algorithms that improve decision making for antibiotic resistant infections. His medium term goal is to deploy these models at scale and evaluate their real-world utility with randomized trials. The candidate's long term goal is to use these algorithms and the infrastructure necessary to maintain them as the technologic basis, of a learning health system that provides personalized decision support at the provider and public health level.

项目摘要/摘要 抗生素耐药性细菌的感染是现代医疗面临的最大挑战之一。 抗生素使用不合理是导致抗生素耐药性的主要原因之一，然而临床医生缺乏强有力的工具。 帮助他们在病人层面做出合理的治疗决定。这个为期3年的临床指导课程的目标是 科学家研究职业发展计划是将最先进的机器学习模型应用于常规 在电子健康记录中收集数据以预测之前的抗菌素耐药性(AMR)风险，以及 在抗生素治疗后。候选人Sanjat Kanjilal博士发现了两个重要的临床问题， 改进对AMR的风险预测可能会对护理质量产生重大影响。第一个是过度使用 社区首发脓毒症患者的广谱抗生素治疗。为了解决这个问题，候选人 将开发一套基于常规收集的电子数据的机器学习预测模型 健康记录，帮助临床医生确定哪种抗生素(S)在治疗患者病原体时有效 光谱最窄的。第二个问题是无法评估患者发展的风险 抗生素治疗后的一种抗药性感染。候选人提议建立一个强大的 目标最大似然估计与机器学习相结合的因果推理模型 评估服用各种常用门诊抗生素对药物开发风险的影响 在治疗后12个月内耐受感染。这项工作的结果将形成一个精确的基础 抗生素管理和治疗的医学方法。 应聘者为传染病临床执业医师和临床医学副主任。 布里格姆妇女医院的微生物学实验室。他以前有制造机器的经验。 提供强大的抗菌管理能力的学习算法。他独特的背景与富人相结合 哈佛医学院和哈佛大学人口医学系的支持环境 朝圣者健康护理研究所，为他过渡到独立资助的临床医生做好准备- 从事传染病和机器学习研究的科学家。他已经组建了一支 由败血症流行病学、抗菌药物管理、 实现科学，机器学习和因果推理，帮助他实现他的目标，并已确定 一项全面的培训计划，为他提供成为该领域领导者所需的技能。他的短片 学期目标是成为改进决策的机器学习算法开发方面的专家 造成抗生素耐药感染。他的中期目标是大规模部署这些模型并进行评估 他们在现实世界中的效用与随机试验。候选人的长期目标是使用这些算法和 必要的基础设施，以维护它们作为技术基础，提供 提供者和公共卫生层面的个性化决策支持。