ClinEX - Clinical Evidence Extraction, Representation, and Appraisal

ClinEX - 临床证据提取、表示和评估

• 批准号: 10754029
• 负责人: Yong Chen
• 金额: $ 70.88万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10754029
• 关键词: Adoption; Artificial Intelligence; Benchmarking; COVID-19 pandemic; COVID-19 patient; Clinical; Clinical Research; Clinical Trials; Collaborations; Data; Data Set; Databases; Detection; Eligibility Determination; Engineering; Ethics; Evaluation; Evidence Based Medicine; Expert Systems; FAIR principles; Face; Generations; Goals; Grain; Health; Healthcare; Human; Individual; Informatics; Information Retrieval; Intelligence; Intervention; Knowledge; Link; Literature; Measures; Mechanical ventilation; Meta-Analysis; Metadata; Methods; Misinformation; Modeling; Natural Language Processing; Natural Language Processing pipeline; Outcome; Patients; Peer Review; Persons; Policy Maker; Population; Probability; PubMed; Public Health; Publications; Publishing; Qualifying; Randomized, Controlled Trials; Registries; Reporting; Research; Research Design; Research Personnel; Resources; Retrieval; SARS-CoV-2 infection; Sample Size; Science; Source; Symbiosis; System; Technology; Testing; Text; Trust; Update; Work; augmented intelligence; clinical trial protocol; clinically relevant; cognitive task; cohort; data repository; data reuse; data sharing; data translator; deep learning; design; drug repurposing; evidence base; experience; fitness; graph knowledge base; human study; improved; interoperability; knowledge base; knowledge graph; mortality risk; novel; severe COVID-19; student mentoring; study characteristics; systematic review; task analysis; user centered design; wasting

SUMMARY Evidence-based medicine faces increasingly mounting challenges. With the explosively growing scientific literature, it will be harder than ever to identify the best evidence available, especially given the large volume of non-traditional and emerging sources of evidence: e.g., evidence derived from trial registries and data repositories; observational datasets; publications without peer review; and scientific blogging. Individual studies using conventional methods for evidence generation, especially randomized controlled trials, may be significantly flawed in their planning, conduct, analysis, or reporting, resulting in ethical violations, wasted scientific resources, and dissemination of misinformation with subsequent health harm. Furthermore, a new randomized controlled trial should be initiated or interpreted in the context of the existing evidence. However, clinical evidence extraction, appraisal, and aggregation remain laborious human tasks given its free-text format. To support evidence-based research so that new research hypothesis selection and testing can be well-grounded on the existing scientific literature and existing evidence can be easily accessible and computable to researchers, patients, or clinicians, we will develop novel, scalable, and generalizable methods for clinical evidence extraction and appraisal so that we can help the public identify reliable evidence easily. We will contribute computable evidence representations and accompanying natural language processing pipelines, achieving symbiosis between the two to support core tasks for evidence-based medicine, such as faceted evidence retrieval (e.g., “retrieve all the randomized controlled trials publications about the efficacy of HCQ on severe COVID-19 patients, with each study having a sample size over 200”), extraction and representation of clinical findings (e.g., “HCQ for people infected with COVID-19 has little or no effect on the risk of death, and probably no effect on progression to mechanical ventilation”), and evidence quality ranking and biases detection. Therefore, we propose four specific aims: Aim 1. — Represent and extract Population, Intervention, Comparison, and Outcome (PICO) information. Aim 2. — Represent and extract clinical findings and their metadata relevant for evidence quality ranking and study biases detection. Aim 3. — Develop and validate an extensible living clinical evidence knowledge graph based on the FAIR principles. Aim 4. — Develop and validate an Augmented Intelligence (AI) system for evidence appraisal. INNOVATION There is no scalable and generalizable informatics solution for literature-based, fine-grained clinical evidence extraction and representation, evidence quality ranking, evidence biases detection, and user- augmented clinical evidence aggregation and appraisal. ClinEX will be the first solution to achieve these goals.

摘要 循证医学面临着越来越大的挑战。随着爆炸性增长的科学 文献，它将比以往任何时候都更难确定可用的最佳证据，特别是考虑到 非传统和新兴证据来源：例如，来自审判登记处和数据的证据 存储库；观测数据集；未经同行审查的出版物；以及科学博客。个别研究 使用常规方法生成证据，特别是随机对照试验，可能会显著 在计划、行为、分析或报告方面存在缺陷，导致违反道德规范，浪费科学资源， 以及传播错误信息，从而损害健康。此外，一种新的随机对照 审判应在现有证据的背景下启动或解释。然而，临床证据表明 鉴于其自由文本格式，提取、评估和聚合仍然是繁重的人工任务。支持 以证据为基础的研究，以便新研究假设的选择和检验可以基于 现有的科学文献和现有的证据对研究人员来说很容易获得和计算， 患者或临床医生，我们将开发新的、可扩展的和可推广的临床证据提取方法 和鉴定，这样我们就可以帮助公众轻松识别可靠的证据。我们将贡献可计算的 证据表示和伴随的自然语言处理流水线，实现共生 以支持基于证据的医学的核心任务，例如分面证据检索(例如， “检索所有关于氢氯喹酮对重症新冠肺炎患者疗效的随机对照试验出版物， 每项研究的样本量超过200“)，临床发现的提取和表示(例如 对于感染新冠肺炎的人来说，对死亡风险几乎没有影响，很可能对 进展到机械通风“)，证据质量排名和偏差检测。 因此，我们提出四个具体目标： 目标1-表示和提取人口、干预、比较和结果(PICO)信息。 目标2-表示和提取与证据质量排名相关的临床研究结果及其元数据 和学习偏差检测。 目标3.开发并验证基于FAIL的可扩展活的临床证据知识图谱 原则。 目标4-开发并验证用于证据鉴定的增强智能(AI)系统。 创新对于基于文献的细粒度信息学解决方案，没有可扩展和可推广的解决方案 临床证据提取和表示、证据质量排名、证据偏差检测和用户- 加强了临床证据的收集和评估。ClinEX将是实现这些目标的第一个解决方案。