SCH: INT: Personalized Real-Time Learning of Optimal Diagnostic Tests using Multi-Modal Clinical Data

该项目利用电子健康记录中捕获的越来越多的数据来发现单个患者的最佳诊断路径。一个在决策建模、放射学/临床实践、信息学和机器学习方面拥有专业知识的多学科团队将研究将数据转化为可操作知识的方法，这是由主动从现有临床数据学习的新型临床决策支持算法实现的。该项目的目标是开发和评估数据驱动的决策支持框架，帮助临床医生通过发现最佳操作序列来提供个性化的患者护理，并以及时、准确和经济高效的方式诊断患者。该项目解决了以下方面的挑战：从庞大的纵向患者数据中寻找相关信息；从过去的患者病例中学习行动序列；以及处理医学实践固有的不确定性。新的算法将改进如何使用观察性临床数据来生成证据，从而提高医疗保健服务的提供和效率，并最终实现精准医疗和改善患者结果。一群不同的研究生将以跨学科的方式接受培训，将算法和数据科学的概念转化为具有真实世界临床实用价值的应用程序，并清楚地了解技术和认知挑战。拟议的研究将创建一个通用框架，用于从医疗保健数据中学习，以发现适合个别患者的最佳行动，目标如下：(1)确定应该使用什么诊断程序组合(例如，成像、实验室、活检)来实现准确和及时的诊断，以及以什么顺序；以及(2)证明可以使用现有的数据来学习这种途径，从而使新的方法可以应用于广泛的临床领域。该项目的新颖方面有三个方面：(1)通过一种新颖的自适应学习方法，以不可预测的方式处理未知和随时间变化的环境，以发现最能预测实时采取的后续行动的信息特征。这建立在相关性学习的早期工作基础上，以动态阐明临床特征和可能的行动之间的关系。(2)开发一种新型的强盗算法，该算法不仅能按顺序发现下一个最佳诊断测试，而且还能识别做出明确诊断所需的附加信息。诊断测试的准确性和价值取决于许多因素(例如，技术、患者特征)。考虑到这些因素，该团队将评估来自类似患者的先前信息如何加快学习速度。(3)提供关于选择进行特定诊断检查的风险和好处的置信限。这些置信限很容易被临床医生理解。这一方法的性能和效用将通过一项前瞻性研究来展示，该研究征求医生对给定患者病例的具体建议的反馈，并从医生不遵循系统建议的情况中学习。

This project harnesses the growing amount of data that is captured in the electronic health record to discover the optimal diagnostic pathway for an individual patient. A multidisciplinary team with expertise in decision modeling, radiology/clinical practice, informatics, and machine learning will investigate approaches that transform data into actionable knowledge, enabled by a new class of clinical decision support algorithms that actively learn from available clinical data. The objective of this project is to develop and evaluate a data-driven framework for decision support that helps clinicians to deliver individualized patient care by discovering optimal sequences of actions and to diagnose patients in a timely, accurate, and cost-effective manner. The project addresses challenges related to finding relevant information from large, longitudinal patient data; learning sequences of actions from past patient cases; and handling uncertainty that is inherent to the practice of medicine. The new algorithms will improve how observational clinical data can be used to generate evidence that improves healthcare delivery, efficiency, and ultimately, realizes precision medicine and improves patient outcomes. A diverse group of graduate students will be trained in an interdisciplinary manner to translate algorithms and data science concepts into applications that have real-world clinical utility, and with a clear understanding of the technical and cognitive challenges.The proposed research will create a generalizable framework for learning from healthcare data to discover optimal actions for individual patients with the following objectives: (1) to determine what combination of diagnostic procedures (e.g., imaging, labs, biopsy) should be used to achieve an accurate and timely diagnosis, and in what sequence; and (2) to demonstrate that learning such pathways can be done using available data, allowing the new methodology to be applied in a wide range of clinical domains. Novel aspects of this project are three-fold: (1)Dealing with an environment that is unknown and changing over time in unpredictable ways through a novel adaptive learning approach to discover the most informative features that are predictive of subsequent actions taken in real-time. This builds upon earlier work in relevance learning to dynamically elucidate the relationships between clinical features and possible actions. (2)Developing a new type of bandit algorithm that not only discovers the next best diagnostic test to order, but also identifies additional information that is needed to make a definitive diagnosis. The accuracy and value of diagnostic tests are dependent on many factors (e.g., technology, patient characteristics). The team will assess how prior information from similar patients can speed-up learning given these factors. (3)Providing confidence bounds about the risks and benefits of selecting a specific diagnostic exam to perform. These confidence bounds can be easily understood by clinicians. The performance and utility of this approach will be demonstrated using a prospective study that solicits physician feedback about specific recommendations for a given patient case and learns from situations in which the physician does not follow the system's recommendation.