EAGER: Data-Driven Learning and Decision Making in Healthcare

EAGER：医疗保健领域的数据驱动学习和决策

• 批准号: 1451037
• 负责人: Mohsen Bayati
• 金额: $ 30万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451037&HistoricalAwards=false
• 关键词: EAGER; Data; Driven; Learning; Decision

The goal of this EArly-Grant for Exploratory Research (EAGER) award is to create a new generation of learning and decision making tools in healthcare systems enabled by growing availability of data. Making data-driven learning and decision making an integral part of healthcare has profound potential to both improve the quality of medical care and to reduce healthcare costs. Despite this, state-of-the art methods are still insufficient for achieving broad acceptance, and significant impact of evidence-based decision making in medical settings is lacking. The fundamental methodological and scientific challenges that this project aims to investigate lie at the intersection of multiple disciplines: decision processes, machine learning, and statistics. This project helps advance this multidisciplinary research area and also develops teaching units for training a new cohort of researchers in this high impact space. On the other hand, this project has the potential to impact other domains beyond healthcare. In particular, the emphasis on dynamic learning and optimal decisions from large amount of data make its findings relevant to a number of domains such as finance, marketing, and electronic commerce. Due to advances in technology and government incentives a large amount of data on patients' conditions is becoming available electronically. On the other hand advances in machine learning and statistics allow the design of "predictive systems": algorithms that can learn generalizable patterns by sifting through a large number of patient profiles and provide accurate future forecasts about clinical adverse events, treatment outcomes, or demand for healthcare services. These predictions can be produced in real-time as new data is captured and can help guide decisions in healthcare systems. However, when statistical learning models are used to guide decisions such as medical treatments, clinicians make decisions based on their predictions that can change subsequent patient data that are collected and used to "re-train" the predictive system, thereby updating the forecast probabilities at the presence of new evidence. Current predictive systems in practice fall in one of the following two categories: (1) they are never re-trained post first installation; or (2) they are periodically re-trained with the arrival of new data. However, the first approach leads to poor forecasts when the new data arrives and circumstances around the decisions change. On the other hand, recent advances in the theory of multi-armed bandits, reinforcement learning, and their applications to digital advertising and recommendation systems indicate that the second approach can also lead to low quality predictions due to the endogeneity of the decision making process. The new dynamic-learning and decision making tools developed in this project will be robust against these challenges by proper modeling of the interactions between the data elements, the decisions, and the consequences of the decisions on new data.

EARLY探索性研究奖（EAGER）的目标是通过不断增长的数据可用性，在医疗保健系统中创建新一代的学习和决策工具。使数据驱动的学习和决策成为医疗保健不可或缺的一部分，对于提高医疗保健质量和降低医疗保健成本具有深远的潜力。尽管如此，最先进的方法仍然不足以实现广泛的接受，在医疗环境中缺乏基于证据的决策的重大影响。该项目旨在调查的基本方法和科学挑战位于多个学科的交叉点：决策过程，机器学习和统计学。该项目有助于推进这一多学科研究领域，并开发教学单元，以在这一高影响力的空间中培养新的研究人员。另一方面，该项目有可能影响医疗保健以外的其他领域。特别是，强调从大量数据中进行动态学习和最优决策，使其研究结果与金融，营销和电子商务等多个领域相关。由于技术的进步和政府的鼓励措施，关于病人状况的大量数据正在以电子方式提供。另一方面，机器学习和统计学的进步允许设计“预测系统”：算法可以通过筛选大量患者资料来学习可推广的模式，并提供有关临床不良事件，治疗结果或医疗保健服务需求的准确未来预测。这些预测可以在捕获新数据时实时生成，并有助于指导医疗保健系统的决策。然而，当使用统计学习模型来指导诸如医学治疗的决策时，临床医生基于他们的预测做出决策，这些预测可以改变收集并用于“重新训练”预测系统的后续患者数据，从而在存在新证据时更新预测概率。目前的预测系统在实践中属于以下两类之一：（1）它们在首次安装后从不重新训练;或（2）它们随着新数据的到来而定期重新训练。然而，第一种方法在新数据到来和决策环境发生变化时会导致预测不佳。另一方面，多臂强盗理论、强化学习及其在数字广告和推荐系统中的应用的最新进展表明，由于决策过程的内隐性，第二种方法也可能导致低质量的预测。在这个项目中开发的新的动态学习和决策工具将通过对数据元素、决策以及决策对新数据的影响之间的相互作用进行适当的建模来应对这些挑战。