Use of NLP to Extract Risk Indicators for Immunologic Disease from the Text of EHRs (UNIITE)

使用 NLP 从 EHR 文本中提取免疫疾病的风险指标 (UNIITE)

• 批准号: 10615338
• 负责人: Nicholas L Rider
• 金额: $ 20.94万
• 依托单位: LIBERTY UNIVERSITY, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-18 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10615338
• 关键词: Achievement; Adoption; Algorithms; Automobile Driving; Awareness; Biology; Calibration; Caring; Cessation of life; Childhood; Clinical; Clinical Data; Clinical Immunology; Code; Collection; Coupled; Data; Data Element; Data Set; Defect; Detection; Diagnosis; Diagnostic; Disease; Early Diagnosis; Economics; Electronic Health Record; Elements; Foundations; Future; Goals; Health; Health Personnel; Healthcare Systems; Human; Immune; Immune System Diseases; Immunologics; Immunology; Individual; Infection; International; Knowledge; Laboratories; Language; Lead; Machine Learning; Manuals; Methodology; Methods; Mining; Modeling; Molecular; Morbidity - disease rate; Natural Language Processing; Natural Language Processing pipeline; Ontology; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Phenotype; Positioning Attribute; Process; Rare Diseases; Risk; Risk Assessment; Societies; Statistical Models; Structure; Supervision; System; Techniques; Testing; Text; Time; Work; advanced disease; analytical method; analytical tool; base; biomedical informatics; clinical practice; clinically relevant; cohort; congenital immunodeficiency; cost; digital health; disability; electronic structure; health information technology; health record; improved; innovation; insight; interoperability; large datasets; machine learning framework; machine learning model; model design; mortality; novel; predictive modeling; scale up; structured data; supervised learning; text searching; tool

PROJECT SUMMARY The Health Information Technology for Economic and Clinical Health (HITECH) Act, enabled widespread adoption of electronic health records (EHRs). In lockstep, healthcare systems have seen the impact of biomedical informatics techniques such as natural language processing (NLP) for mining inference and classifying EHR data. Combining digital health records and available analytical tools represents an opportunity to improve care for patients who suffer from rare disease such as primary immune deficiency (PID) where optimal outcomes are predicated upon early detection. However, only a fraction of PID patients receive a diagnosis before sustaining serious infections. This underscores a need for novel methods to improve diagnostic rates and for driving understanding about PID. At present, barriers to detecting PIDs include recognizing heterogeneous clinical features, distinguishing infections in PID from that of the normal host, and general lack of awareness about the diseases. Creating precise analytical methods to mine and make predictions from EHR data represents a potential solution to these challenges. As such our goals are to develop a system for automatic extraction of PID risk indicators from EHR notes for the purpose of improving widespread diagnosis and advancing knowledge about human immunologic disease. Our preliminary work suggests that structured EHR data such as problem list elements and diagnostic codes can be used to develop a probabilistic framework for assessing risk of PID but they are limited and do not exemplify the full range of concepts needed to optimally characterize PID. Data elements mined from text can couple to presently available EHR structured data and ontologies for improved annotation of PID. Building a framework of PID-specific risk indicators will enable NLP approaches for PID detection and improved understanding about human immune dysfunction. The Specific Aims for our proposal are as follows: 1.) To use a data-driven approach for identifying and enumerating PID risk indicators from EHR text. 2.) To develop NLP methods for automatically extracting key PID risk indicators from EHR text. Our proposal leverages a very large corpus of EHR note text captured from over 2000 PID patients prior to their ultimate diagnosis, as well as almost 5000 control patients. Mining this dataset and synergistically using state-of-the-art NLP methodologies will build the foundation of a potent and interoperable text mining system for PID risk detection. We expect this work to advance disease detection, characterization of specific human immune defects and allow for additional inference which combines clinical, laboratory, and molecular information about PID.

项目摘要 《经济和临床健康卫生信息技术法》（HITECH）， 采用电子健康记录（EHR）。在步调一致的情况下，医疗系统已经看到了 生物医学信息学技术，如用于挖掘推理的自然语言处理（NLP）， 分类EHR数据。将数字健康记录与可用的分析工具相结合是一个机会 改善对患有罕见疾病（如原发性免疫缺陷症（PID））的患者的护理， 最佳结果取决于早期发现。然而，只有一小部分PID患者接受 在严重感染之前进行诊断。这强调了需要新的方法来改善诊断 率和驾驶了解PID。目前，检测PID的障碍包括识别 不同的临床特征，区分PID感染与正常宿主感染，以及一般缺乏 对疾病的认识。创建精确的分析方法来挖掘和预测EHR 数据是应对这些挑战的潜在解决方案。因此，我们的目标是开发一个自动化的系统， 从EHR笔记中提取PID风险指标，以改善广泛的诊断， 提高对人类免疫疾病的认识。我们的初步工作表明，结构化EHR 诸如问题列表元素和诊断代码之类的数据可以用于开发概率框架， 评估PID的风险，但它们是有限的，并没有涵盖所需的全部概念， 表征PID。从文本中挖掘的数据元素可以耦合到当前可用的EHR结构化数据， 用于改进PID注释的本体。建立一个特定于PID的风险指标框架将使NLP PID检测的方法和提高对人类免疫功能障碍的理解。具体 我们的建议的目的如下：1）。使用数据驱动的方法来识别和列举PID风险 EHR文本中的指标。2.）的情况。开发NLP方法，用于自动提取关键PID风险指标， 电子病历文本。我们的提案利用了从2000多名PID患者中捕获的非常大的EHR注释文本语料库 以及近5000名对照患者。挖掘这个数据集， 使用最先进的NLP方法将为有效的、可互操作的文本挖掘奠定基础 PID风险检测系统。我们希望这项工作能够推进疾病检测，特异性 人类免疫缺陷，并允许结合临床，实验室和分子 关于PID的信息