Regional Oncology Research Center (LLMs for Unstructured Data Extraction)

区域肿瘤学研究中心（非结构化数据提取法学硕士）

• 批准号: 10891024
• 负责人: WILLIAM George NELSON
• 金额: $ 30万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10891024
• 关键词: Address; Artificial Intelligence; Clinical; Clinical Data; Clinical Management; Data Commons; Data Element; Diagnosis; Engineering; Equity; Healthcare; Individual; Information Retrieval; Language; Malignant Neoplasms; Manuals; Modeling; Molecular; Natural Language Processing; Oncology; Online Systems; Output; Pathologic; Pathology Report; Patients; Performance; Play; Process; Prognosis; Reporting; Research; Research Design; Role; Structure; Supervision; Techniques; Text; Training; Work; cancer diagnosis; cohort; compare effectiveness; design; heuristics; improved; innovation; interdisciplinary approach; medical schools; model building; model development; neoplasm registry; precision medicine; unstructured data

Abstract Artificial intelligence (AI) has the potential to revolutionize healthcare by leveraging clinical data to advance research and improve oncology practice. Within free-text pathology reports, crucial information about primary cancer diagnoses and evolving molecular features is embedded. Extracting and interpreting this information accurately is essential for determining cancer stage, which plays a decisive role in prognosis and guiding clinical management. Although natural language processing (NLP) techniques have been applied to extract focused information from pathology reports, there is still a need for adaptable, generalizable, and interpretable strategies to enhance clinical data abstraction. To address this need, we propose a multidisciplinary approach to develop an integrative clinical information extraction pipeline. This work aims to improve, assess, and enhance the abstraction of relevant features of pathological diagnosis from pathology reports by leveraging large language models. Our research design involves several steps. First, we will establish a diverse and equitable cohort of patients from our Cancer Registry and collect free-text pathology reports, along with structured clinical data obtained from the Johns Hopkins School of Medicine Precision Medicine Analytics Platform (PMAP) Data Commons. Next, we will employ an information extraction platform to identify pathological features from the reports. This platform will utilize a suite of models, including BERT-like models, GPT-3.5, and GPT-4, provided by Microsoft, specifically designed for identifying key cancer attributes. Subsequently, we will evaluate the output of individual models using the CASPER interactive model development framework, enhancing and refining the results through heuristics and weak supervision. The augmented model output will be presented through a web-based user interface, allowing expert curators to provide further input. We will then compare the effectiveness of each CASPER-augmented model and its derived pathological features against the established gold standard annotations from the Cancer Registry. Finally, we will enhance the GPT-based language models based on the assessment, curation, and comparison process, employing prompt engineering techniques to improve performance and mitigate bias.

抽象的 人工智能（AI）有可能通过利用临床数据来促进医疗保健 研究和改善肿瘤学实践。在自由文本病理报告中，有关主要的关键信息 嵌入了癌症诊断和不断发展的分子特征。提取和解释此信息 准确地对于确定癌症阶段至关重要，癌症阶段在预后和指导临床方面起决定性作用 管理。尽管已经应用了自然语言处理（NLP）技术来提取集中精力 病理报告中的信息仍然需要适应性，可解释的策略 增强临床数据抽象。为了满足这一需求，我们提出了一种多学科的方法来发展 综合临床信息提取管道。这项工作旨在改善，评估和增强 通过利用大语言的病理报告的病理诊断相关特征的抽象 型号。 我们的研究设计涉及多个步骤。首先，我们将建立各种各样且公平的患者队列 从我们的癌症注册表中收集自由文本病理报告，以及从 约翰·霍普金斯医学学院精密医学分析平台（PMAP）数据共享。接下来，我们 将采用信息提取平台从报告中识别病理特征。这个平台将 使用Microsoft提供的一套模型，包括Bert样模型，GPT-3.5和GPT-4，特别是 设计用于识别关键癌症属性。随后，我们将评估单个模型的输出 使用Casper Interactive模型开发框架，通过 启发式和监督弱。增强模型输出将通过基于Web的用户呈现 接口，允许专家策展人提供进一步的输入。然后，我们将比较每个人的有效性 Casper aigment Model及其衍生的病理特征针对已建立的黄金标准 癌症注册表的注释。最后，我们将根据 评估，策展和比较过程，采用及时的工程技术来改进 性能和减轻偏见。