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: 10696220
• 负责人: BYRON CASEY WALLACE
• 金额: $ 35.05万
• 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/10696220
• 关键词: Affect; Area; Automated Abstracting; Caring; Clinical; Cognitive; Collaborations; Communication; Complement; Computerized Medical Record; Consult; Data; Data Science; Decision Making; Dedications; Diagnosis; Diagnostic; Diagnostic Errors; Diagnostic Imaging; Differential Diagnosis; Distant; Electronic Health Record; Feedback; Glean; Hospitals; Image; Image Analysis; 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; contextual factors; design; heuristics; impression; improved; insight; machine learning model; medical specialties; multimodality; natural language; neural; novel; point of care; prototype; radiologist; recruit; 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.

项目概要 电子病历的有针对性的神经文本摘要以改善成像 诊断 电子健康记录 (EHR) 包含丰富的患者信息，可为诊断提供信息 然而，大部分信息都是非结构化的（即自由文本）。 使得很难在冗长的篇幅中找到少数相关注释来为给定的决定提供信息 患者记录，进而呈现隐藏在 EHR 中的关键信息，域几乎无法访问 操作专家在时间有限的情况下进行检查，往往无法做出临床决策。 我们建议设计、训练和部署新颖的自然语言处理。 (NLP) 模型，提供 EHR 内自由文本数据的提取摘要 特定查询；我们还建议此类模型能够帮助诊断和决策。 使用这些模型来尝试抵消领域专家给临床实践带来的认知偏差。 我们特别关注放射学的重要和说明性领域，尽管该方法将 进行影像诊断的放射科医生没有足够的时间进行推广。 仔细阅读电子病历中存储的患者病史，他们必须将就着用有限的资料； 解释成像时的背景信息我们将建立在我们的初步模型之上。 总结从 EHR 中提取的可能支持特定诊断的文本证据。 我们设想了一个交互式系统，放射科医生使用该模型来显示文本 支持成像可能暗示的不同潜在条件的证据。 放射科医生（和其他领域专家）在做出决策时依赖启发法（第一类思维） 在时间限制下，这会导致各种影响诊断的认知偏差。 已被证明是放射学中很大一部分诊断错误的根源。我们提出了一种新颖的方法。 二次使用为本项目开发的 NLP 模型作为抵消这些问题的手段 具体来说，一旦放射科医生通过自然方式表明了初步的潜在诊断。 语言查询时，我们将自动呈现一些替代的合理诊断和摘要 提取支持这些的证据（以及与初始查询相关的证据摘要）。 这些替代诊断将从全部或已发布的鉴别诊断列表中收集，并且 我们将根据训练有素的模型，按照当前患者的预测概率对它们进行重新排序 我们将在布莱根妇女医院的实践中评估所提出的模型。 医院，并评估整合自动生成的摘要实际影响的程度 护理点的临床决策。