Development of Accurate and Interpretable Machine Learning Algorithms for their application in Medicine

开发准确且可解释的机器学习算法以应用于医学

• 批准号: 10630752
• 负责人: Gilmer Valdes
• 金额: $ 19.2万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-07 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10630752
• 关键词: Address; Adjuvant Radiotherapy; Algorithms; Area; Benchmarking; Biometry; Cancer Patient; Clinical; Development; Doctor of Philosophy; Foundations; Goals; Healthcare; Hospitals; Institution; Intensive Care Units; Knowledge; Laws; Linear Models; Machine Learning; Malignant neoplasm of prostate; Mathematics; Medicine; Mentors; Modeling; Modernization; Nature; Public Health; Radiation Oncology; Research; Research Personnel; Research Project Grants; Risk; San Francisco; Technical Expertise; Training; Validation; Work; anticancer research; clinical decision-making; clinical practice; information organization; machine learning algorithm; mortality; multidisciplinary; next generation; quality assurance; statistics; venous thromboembolism

Project Summary The objective of this proposal is to provide a robust course of training for Gilmer Valdes, PhD, DABR, a candidate with an excellent foundation in clinical and machine learning research, to enable him to become an independent investigator. The proposed research aims to address a tradeoff between interpretability and accuracy of modern machine learning algorithms which limits their use in clinical practice. The candidate’s central hypothesis is that the current tradeoff is not a law of nature but rather a limitation of current interpretable machine learning algorithms. Towards proving this hypothesis, the candidate, leading a multidisciplinary team, have developed unique mathematical frameworks (MediBoost and the Conditional Interpretable Super Learner) to build interpretable and accurate models. The proposed research will I) implement and extensively benchmark these frameworks and II) use the algorithms develop to solve three clinical problems where potentially suboptimal models are currently used to make clinical decisions: 1) predicting mortality in the Intensive Care Unit, 2) predicting risk of Hospital Acquired Venous Thromboembolism, 3) predicting which prostate cancer patients benefit the most from adjuvant radiotherapy. The candidate’s training and research plan, multidisciplinary by nature, takes advantage of the proximity of UC San Francisco, Stanford and UC Berkeley and proposes a training plan that cannot be easily replicated elsewhere. Recognizing the multidisciplinary nature of the work proposed, the author will be mentored and work closely with a stellar committee from three institutions and different scientific areas (Machine Learning, Biostatistics, Statistics, Hospital Medicine, Cancer Research and Quality Assurance in Medicine): Jerome H. Friedman PhD (Stanford Statistics Department), Mark Van der Laan PhD (Berkeley Biostatistics and Statistics Department), Mark Segal (UCSF Epidimiology and Biostatistics Deparments), Andrew Auerbach MD (UCSF Medicine Department), Felix Y. Feng MD (UCSF Radiation Oncology),and Timothy D. Solberg PhD (UCSF Radiation Oncology). This committee will be coordinated by Dr Solberg. The candidate also counts with a strong a multidisciplinary team of collaborators. Successful completion of the proposed research will develop the next generation of accurate and interpretable Machine Learning algorithms and solve three important clinical problems where linear models are currently used in clinical settings. This proposal has wide-ranging implications across the healthcare spectrum. The intermediate-term goal is for the candidate to acquire the knowledge, technical skills and expertise necessary to submit a successful R01 proposal.

项目摘要 本提案的目的是为Gilmer Valdes博士，DABR，a 候选人在临床和机器学习研究方面拥有良好的基础，使他能够成为一名 独立调查员拟议的研究旨在解决可解释性和 现代机器学习算法的准确性限制了它们在临床实践中的使用。候选人的 中心假设是，电流的权衡不是自然规律，而是电流的限制。 可解释的机器学习算法。为了证明这一假设，候选人，领导一个 多学科团队，已经开发出独特的数学框架（MediBoost和条件 可解释的超级学习者）来构建可解释的和准确的模型。拟议的研究将I） 实现并广泛地对这些框架进行基准测试，以及II）使用开发的算法来解决三个 当前使用潜在次优模型来做出临床决策的临床问题：1） 预测重症监护室的死亡率，2）预测医院获得性静脉炎的风险 血栓栓塞，3）预测哪些前列腺癌患者从辅助放疗中获益最大。 候选人的培训和研究计划，多学科的性质，利用加州大学的邻近 旧金山弗朗西斯科，斯坦福大学和加州大学伯克利分校，并提出了一个培训计划，不能轻易复制 其他地方认识到拟议工作的多学科性质，作者将得到指导， 与来自三个机构和不同科学领域的恒星委员会密切合作（机器学习， 生物统计学，统计学，医院医学，癌症研究和医学质量保证）：杰罗姆H。 Friedman博士（斯坦福大学统计系），Mark货车der Laan博士（伯克利生物统计学和统计学 部门），马克西格尔（加州大学旧金山分校流行病学和生物统计学），安德鲁奥尔巴赫医学博士（加州大学旧金山分校 医学部），Felix Y. Feng MD（UCSF放射肿瘤学）和Timothy D. Solberg博士（UCSF 放射肿瘤学）。该委员会将由Solberg博士协调。候选人还以 强大的多学科合作团队。成功完成拟议的研究将开发 下一代准确和可解释的机器学习算法，并解决三个重要的 临床问题，其中线性模型目前用于临床设置。这一建议具有广泛的 对整个医疗保健领域的影响。中期目标是让候选人获得 成功提交R01建议书所需的知识、技能和专业知识。