Developing and Evaluating Multi-Modal Clinical Diagnostic Reasoning Models for Automated Diagnosis Generation

项目摘要 在美国，诊断错误影响了1200万患者，每年导致8万人死亡。主要原因 对于诊断错误，包括医疗保健提供者引入的认知偏差， 医疗团队，无法访问关键数据，以及无法识别电子医疗中的时间敏感数据 录制(EHR)。电子病历中信息过载带来的认知负担导致临床医生做出决定 带有偏颇启发式方法的捷径和错过EHR中的关键数据，导致错失及时和 准确的诊断。人工智能(AI)和临床自然语言处理(CNLP)提供 有机会帮助理解医学文本，并可以自动化电子病历分析，为未来指明了方向 像人类一样运用医学知识和临床经验。然而，cNLP的大多数任务 不是为床边应用程序而设计的，以生成诊断并增强床边决策。我们有 收集了初步数据并设计了用于临床诊断推理的cNLP基准任务。我们的任务 在本建议书中介绍构建模型的关键认知过程，该模型可以合成电子病历数据以生成 符合循证医学和医学知识表示的诊断。该提案旨在 开发新的cNLP模型，理解和集成多模式EHR数据，并进行推理 建立比当前神经网络更高精度的模型的大规模医学知识库 模特们。我将首先开发一个同时读取结构化和非结构化电子病历的多模式生成模型 使用两个阶段的培训过程(目标1)输出诊断数据。在另一个目标中，我将构建一个 使用神经符号方法从医学概念和关系中获取知识库 国家医学图书馆统一医学语言系统(UMLS)。知识库将成为 根据EHR收集的每日护理笔记中的信息生成诊断的模型(目标2)。这个 第三个目标将使用以人为中心的设计来设计和试行临床诊断决策支持系统 原则。AIMS 1和AIMS 2的最佳模型将由临床医生在 使用先前经过验证的仪器确保患者安全和诊断错误的系统(目标3)。已完成的 AIMS将为未来的临床研究提供信息，开发NLP驱动的临床决策支持工具，以减少 诊断错误。我将在我的合作导师和顾问的直接监督下完成这个项目 在开发临床神经语言模型、在卫生系统中实施人工智能驱动的工具方面的专业知识， 以及具有增强智能的临床决策支持系统。这个多学科团队共同带来了 在临床信息学方面拥有全国知名的专业知识，并有成功指导的记录。我的四年 建议加强指导，临床研究培训，卫生系统工程的正式课程 和信息学，以及威斯康星大学麦迪逊分校的计算资源将确保我的成功 成长为一名独立的科学家。

PROJECT ABSTRACT Diagnostic errors affect 12 million patients in the U.S. and contribute to 80,000 deaths per year. The main causes for diagnostic errors include cognitive biases introduced by healthcare providers, miscommunication between healthcare teams, lack of access to key data, and not recognizing time-sensitive data in the electronic health record (EHR). The cognitive burden from information overload in the EHR cause clinicians to take decisional shortcuts with biased heuristics and miss critical data in the EHR, leading to missed opportunities for timely and accurate diagnoses. Artificial Intelligence (AI) and clinical Natural Language Processing (cNLP) provide opportunity to help understand medical text and can automate EHR analysis, pointing to the promising direction of invoking medical knowledge and clinical experience as humans do. However, the majority of the cNLP tasks are not designed for bedside application to generate diagnoses and augment bedside decision-making. We have have gathered preliminary data and designed cNLP benchmark tasks for clinical diagnostic reasoning. Our tasks address key cognitive processes to build models in this proposal that can synthesize EHR data to generate diagnoses that align with evidence-based medicine and medical knowledge representation. The proposal aims to develop novel cNLP models that understand and integrate multi-modal EHR data, and conduct reasoning over a large-scale medical knowledge base to build a model that provides higher accuracy than current neural network models. I will first develop a multi-modal generative model that reads in both structured and unstructured EHR data to output diagnoses using a two-stage training process (Aim 1). In a separate aim, I will construct a knowledge base using a neural symbolic approach from medical concepts and relations sourced from the National Library of Medicine's Unified Medical Language System (UMLS). The knowledge base will be part of the model to generate diagnoses given the information from a daily care note collected in the EHR (Aim 2). The third aim will design and pilot a clinical diagnostic decision support system using human-centered design principles. The best models from Aims 1 and 2 will be evaluated for diagnostic accuracy by clinicians in the system using previously validated instruments for patient safety and diagnostic error (Aim 3). Completion of the aims will inform future clinical studies on developing NLP-driven clinical decision support tools for reducing diagnostic error. I will complete this project under the direct supervision of my co-mentors and advisors who have expertise in developing clinical neural language models, implementation of AI-driven tools in health systems, and clinical decision support systems with augmented intelligence. Together, this multidisciplinary team brings nationally renowned expertise in clinical informatics with a track record of successful mentorship. My 4-year proposal with intensive mentorship, clinical research training, formal coursework in health systems engineering and informatics, and computing resources at the University of Wisconsin-Madison will ensure my success as I grow into an independent scientist.