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

项目摘要 在美国，诊断错误影响着1200万患者，每年导致8万人死亡。的主要原因 诊断错误包括医疗保健提供者引入的认知偏见， 医疗保健团队，缺乏对关键数据的访问，以及无法识别电子医疗保健中的时间敏感数据 记录（EHR）。EHR中信息过载的认知负担导致临床医生做出决策 在EHR中使用有偏见的统计学和错过关键数据的捷径，导致错过及时和 准确的诊断。人工智能（AI）和临床自然语言处理（cNLP）提供了 有机会帮助理解医疗文本，并可以自动化EHR分析，指出了有前途的方向 像人类一样调用医学知识和临床经验。然而，大多数cNLP任务 不是为床边应用而设计的，以生成诊断和增强床边决策。我们有 已经收集了初步数据，并为临床诊断推理设计了cNLP基准任务。我们的任务 解决关键的认知过程，以建立模型，在这个建议，可以综合EHR数据，以产生 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，理解和整合多模态EHR数据，并进行推理， 一个大规模的医学知识库，以建立一个模型，提供比目前的神经网络更高的准确性 模型我将首先开发一个多模态生成模型，它可以读取结构化和非结构化EHR 使用两阶段训练过程（Aim 1）输出诊断数据。在另一个目标中，我将构建一个 知识库使用神经符号的方法从医学概念和关系来源于 国家医学图书馆统一医学语言系统（UMLS）。知识库将成为 该模型根据EHR中收集的日常护理记录中的信息生成诊断（目标2）。的 第三个目标是采用以人为本的设计，设计并试验一个临床诊断决策支持系统 原则目标1和2中的最佳模型将由临床医生评估诊断准确性， 系统使用先前确认的仪器，以确保患者安全和诊断错误（目标3）。完成 目的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共同导师和顾问的直接监督下完成这个项目， 开发临床神经语言模型的专业知识，在卫生系统中实施人工智能驱动的工具， 和具有增强智能的临床决策支持系统。这支多学科团队将为 在临床信息学方面拥有全国知名的专业知识，并有成功的指导记录。我4岁的 建议与密集的指导，临床研究培训，正式课程的卫生系统工程 威斯康星大学麦迪逊分校的信息学和计算资源将确保我的成功， 成长为独立的科学家。

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.