Targeted Neural Text Summarization of Electronic Medical Records to Improve Imaging Diagnostics

电子病历的定向神经文本摘要可改善影像诊断

• 批准号: 10443224
• 负责人: BYRON CASEY WALLACE
• 金额: $ 35.88万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-02 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10443224
• 关键词: Affect; Area; Automated Abstracting; Caring; Clinical; Cognitive; Complement; Computerized Medical Record; Consult; Data; Data Science; Decision Making; Diagnosis; Diagnostic; Diagnostic Errors; Diagnostic Imaging; Differential Diagnosis; Distant; Electronic Health Record; Feedback; Glean; Gold; Hospitals; Image; Lead; MRI Scans; Machine Learning; Measures; Medical; Medical History; Medical Imaging; Methods; Modeling; Modernization; Natural Language Processing; Output; Patients; Performance; Probability; Provider; Publishing; Radiology Specialty; Recording of previous events; Records; Reporting; Research; Retrieval; Running; Source; Specialist; Supervision; Surface; System; Text; Therapeutic; Thinking; Time; Training; Woman; Work; X-Ray Computed Tomography; accurate diagnosis; clinical center; clinical decision-making; clinical diagnostics; clinical practice; design; heuristics; impression; improved; insight; machine learning model; medical specialties; multimodality; natural language; novel; point of care; prototype; radiologist; recruit; relating to nervous system; unstructured data

Project Summary Targeted Neural Text Summarization of Electronic Medical Records to Improve Imaging Diagnosis Electronic health records (EHRs) contain a wealth of patient information that might inform diagnostic and therapeutic decision-making. However, much of this information is unstructured (i.e., free-text). This makes it difficult to find the few relevant notes that might inform a given decision amongst lengthy patient records, in turn rendering key information buried within EHR practically inaccessible to domain experts operating under time constraints. Consequently, clinical decisions are often made without the benefit of all available data. We propose to design, train, and deploy novel natural language processing (NLP) models that provide extractive summaries of the free-text data within EHR conditioned on particular queries; the intent is for such models to aid diagnosis and decision-making. We also propose to use these models to try and counteract the cognitive biases that domain experts bring to clinical practice. We focus specifically on the important and illustrative area of radiology, although the approach will generalize to other specialties. Radiologists performing imaging diagnosis do not have adequate time to carefully read through patient histories stored within EHR; they must instead make do with limited background information when interpreting imaging. We will build on our preliminary on models that summarize textual evidence extracted from EHR that might support particular hypothesized diagnoses. We envision an interactive system in which this model is used by the radiologist to surface textual evidence that supports different potential conditions that might be suggested by the imaging. Radiologists (and other domain experts) rely on heuristics — type 1 thinking — when making decisions under time constraints. This results in various cognitive biases influencing diagnoses, and these have been shown to be the source of a significant fraction of diagnostic errors in radiology. We propose a novel secondary use of the NLP models to be developed for this project as a means of counteracting these cognitive biases. Specifically, once the radiologist has indicated an initial potential diagnosis via a natural language query, we will automatically present a few alternative plausible diagnosis and summaries of the extracted evidence supporting these (alongside the summary of evidence relevant to the initial query). These alternative diagnoses will be gleaned from gamuts or published lists of differential diagnoses, and we will re-rank them in order of their predicted probability for the current patient according a trained machine learning model. We will evaluate the proposed models in practice at Brigham and Women's Hospital, and assess the degree to which integrating automatically generated summaries actually affects clinical decision-making at point of care.

项目摘要 针对电子病历的有针对性的神经文本摘要以改进成像 诊断 电子健康记录（EHRS）包含大量患者信息，这些信息可能会为诊断提供信息 和热决策。但是，这些信息的大部分是非结构化的（即自由文本）。这 很难找到一些可能在冗长的决定中告知给定决定的相关笔记 患者记录，反过来渲染在EHR中构建的关键信息实际上无法访问域 在时间限制下运行的专家。因此，通常没有 所有可用数据的好处。我们建议设计，训练和部署新颖的自然语言处理 （NLP）提供EHR中自由文本数据的提取性摘要的模型 特定的查询；目的是使这种模型有助于诊断和决策。我们还建议 使用这些模型尝试抵消领域专家为临床实践带来的认知偏见。 我们专门关注放射学的重要和说明性领域，尽管该方法将 推广到其他专业。执行成像诊断的放射科医生没有足够的时间 仔细阅读EHR中存储的患者历史；他们必须以有限的方式做 解释成像时的背景信息。我们将以我们的初步为基础的模型 从EHR提取的摘要文本证据可能支持特定假设的诊断。 我们设想了一个交互式系统，其中辐射派使用该模型来表面文本 支持成像可能建议的不同潜在条件的证据。 放射科医生（和其他领域专家）在做出决定时依靠启发式方法 - 1型思维 在时间限制下。这导致各种认知偏见会影响诊断，这些偏见具有 我们提出了一本小说 为该项目开发的NLP模型的次要使用，以此来抵消这些模型 认知偏见。具体而言，一旦放射科医生表明了通过自然的初始潜在诊断 语言查询，我们将自动提供一些替代性的合理诊断和摘要 提取了支持这些的证据（以及与初始查询有关的证据摘要）。 这些替代诊断将从范围或已发布的差异诊断列表中收集，以及 我们将根据受过训练 机器学习模型。我们将在Brigham和妇女的实践中评估拟议的模型 医院，并评估整合自动产生的摘要实际影响的程度 护理点的临床决策。