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

项目摘要 这项提议的目标是为Gilmer Valdes、博士、DABR、a 在临床和机器学习研究方面有出色基础的候选人，使他能够成为 独立调查员。这项拟议的研究旨在解决可解释性和可解释性之间的权衡 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 核心假设是，当前的权衡不是自然规律，而是对当前的限制 可解释的机器学习算法。为了证明这一假设，这位候选人领导着一个 多学科团队，开发了独特的数学框架(MediBoost和Conditional 可解释的超级学习者)来构建可解释和准确的模型。拟议的研究将I) 实施这些框架并广泛进行基准测试，ii)使用开发的算法解决三个问题 当前使用潜在的次优模型来做出临床决策的临床问题：1) 预测重症监护病房的死亡率，2)预测医院获得性静脉血栓的风险 血栓栓塞症，3)预测哪些前列腺癌患者从辅助放射治疗中获益最多。 候选人的培训和研究计划本质上是多学科的，利用了加州大学的邻近优势 旧金山、斯坦福大学和加州大学伯克利分校提出了一项不易复制的培训计划 其他地方。认识到拟议工作的多学科性质，提交人将得到指导和 与来自三个机构和不同科学领域的杰出委员会密切合作(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)：Jerome H. 弗里德曼博士(斯坦福大学统计系)，马克·范德兰博士(伯克利生物统计和统计) 马克·西格尔(加州大学旧金山分校表现学和生物统计部)，安德鲁·奥尔巴赫医学博士(加州大学洛杉矶分校 医学系)、Felix Y.冯医学博士(加州大学旧金山分校放射肿瘤学)和蒂莫西·D·索尔伯格博士(加州大学旧金山分校 放射肿瘤学)。该委员会将由索尔伯格博士协调。候选人也要考虑到 强大的多学科合作者团队。成功完成拟议的研究将进一步发展 下一代精确和可解释的机器学习算法及解决三个重要问题 目前在临床环境中使用线性模型的临床问题。这项建议范围很广。 医疗保健领域的影响。中期目标是候选人获得 提交成功的R01建议书所需的知识、技术技能和专业知识。

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.