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.

概括 循证医学面临越来越多的挑战。随着爆炸性发展的科学 文学，确定可用的最佳证据会比以往任何时候都更难 非传统和新兴证据来源：例如，源自试验注册和数据的证据 存储库；观察数据集；没有同行评审的出版物；和科学博客。个人研究 使用常规方法进行证据，尤其是随机对照试验，可能是显着的 他们的计划，行为，分析或报告有缺陷，导致违反道德的行为，浪费了科学资源， 以及随后的健康危害传播未经信息。此外，新的随机控制 试验应在现有证据的背景下开始或解释。但是，临床证据 鉴于其自由文本格式，提取，评估和聚合仍然是实验室人类任务。支持 基于证据的研究，因此可以在 现有的科学文献和现有证据可以轻松地访问和计算。 患者或临床医生，我们将开发出新颖，可扩展和可推广的临床证据提取方法 和评估，以便我们可以帮助公众轻松识别可靠的证据。我们将贡献可计算的 证据表示和参与自然语言处理管道，实现符号 在两者之间支持基于循证医学的核心任务，例如面对证据检索（例如， “检索所有关于HCQ对严重COVID-19患者的有效性的随机对照试验出版物， 每项研究的样本量超过200英寸），提取和临床发现的表示（例如，“ HCQ） 对于感染Covid-19的人，对死亡风险几乎没有影响，可能不会影响 进展到机械通气”），以及证据质量排名和偏见检测。 因此，我们提出了四个具体目标： 目标1。 - 代表和提取种群，干预，比较和结果（PICO）信息。 目标2。 - 代表并提取与证据质量排名有关的临床发现及其元数据 并研究偏见检测。 目标3。 - 基于博览会发展并验证可扩展的生活临床证据知识图 原则。 目标4。 - 开发并验证增强情报（AI）系统以进行证据评估。 创新没有针对基于文学的，细粒度的可扩展性和可推广的信息解决方案 临床证据提取和表示，证据质量排名，证据偏见检测以及用户 - 增强临床证据汇总和评估。 Clinex将是实现这些目标的第一个解决方案。