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途径。一个在决策建模，放射学/临床实践，信息学和机器学习方面具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，这些方法由一类新的临床决策支持算法实现，这些算法可以从现有的临床数据中积极学习。该项目的目标是开发和评估一个数据驱动的决策支持框架，帮助临床医生通过发现最佳的行动顺序来提供个性化的患者护理，并以及时，准确和具有成本效益的方式诊断患者。该项目解决了从大型纵向患者数据中查找相关信息的挑战;从过去的患者案例中学习行动序列;以及处理医学实践中固有的不确定性。新算法将改善观察性临床数据如何用于生成证据，以改善医疗服务，提高效率，并最终实现精准医疗和改善患者预后。一群不同的研究生将接受跨学科的培训，将算法和数据科学概念转化为具有现实临床实用性的应用，并对技术和认知挑战有清晰的理解。拟议的研究将创建一个可推广的框架，用于从医疗数据中学习，以发现个体患者的最佳行动，目标如下：（1）确定诊断程序的什么组合（例如，影像学、实验室、活检）应用于实现准确和及时的诊断，以及以何种顺序;以及（2）证明可以使用可用数据来完成对此类途径的学习，从而允许新方法学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来处理未知的环境，并以不可预测的方式随着时间的推移而变化，以发现最具信息性的特征，这些特征可以预测实时采取的后续行动。这建立在相关学习的早期工作，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bandit算法，不仅可以发现下一个最好的诊断测试，而且还可以识别出做出明确诊断所需的额外信息。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）。该团队将评估来自类似患者的先前信息如何在考虑到这些因素的情况下加速学习。（3）提供关于选择要执行的特定诊断检查的风险和益处的置信界限。这些置信界限可以很容易地被临床医生理解。这种方法的性能和效用将使用前瞻性研究来证明，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.