Automated Indexing for Publication Types and Study Designs

出版物类型和研究设计的自动索引

• 批准号: 10715907
• 负责人: NEIL R SMALHEISER
• 金额: $ 32.17万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-02 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715907
• 关键词: Animal Model; Automated Indexing; Bibliographic Databases; Data; Databases; Explosion; Guidelines; Health; Human; Infrastructure; Intervention; Journals; Literature; Manuals; Manuscripts; Medical; Methods; Modeling; Performance; Pharmacologic Substance; Printing; PubMed; Publications; Publishing; Research Design; Research Personnel; Retrieval; Students; Text; United States National Library of Medicine; effectiveness study; human disease; human model; improved; indexing; machine learning method; machine learning model; neural; novel therapeutics; preclinical study; prototype; systematic review; tool; vector

Project Summary/Abstract Retrieving biomedical articles from bibliographic databases requires accurate, detailed indexing of the topics that are discussed as well as their publication types and study designs. It is difficult for indexers to keep up with manual assignments in view of the explosion of published literature. Although NLM has recently employed automatic machine learning methods to index articles according to the major topics discussed, there is still no automatic means of indexing each article across all publication types and study designs. We have recently created a working prototype tool, Multi-Tagger, which has assigned probabilistic predictive scores for all PubMed articles for 50 different publication types and study designs (collectively, PTs). We now propose to develop Multi-Tagger 2.0, to handle a wider variety of study designs, articles, users and use cases, and to ensure that the data are disseminated in a form that is appropriate to each different type of user. Specifically, we aim to: Aim 1. Optimize methods for assigning Publication Types and study designs to both PubMed and non- PubMed biomedical articles, preprints and manuscripts. Aim 2. Evaluate PTs in detail, taking into account model performance, use cases and users. Aim 3. Optimize dissemination of PT predictive scores by query interface and API. Aim 4. Explore how to integrate Multi-Tagger with other tools for automating evidence synthesis. The proposed studies will greatly enhance retrieval of relevant articles and preprints across multiple databases, and will be useful for a wide range of biomedical end-users (clinicians, researchers, students and journal editors) as well as user groups including systematic review groups, bibliographic database managers, those studying preclinical animal models of human disease, and pharmaceutical companies developing new drug treatments. Improving the infrastructure of the biomedical literature will thus indirectly impact on human health.

项目概要/摘要 从书目数据库检索生物医学文章需要准确、详细的主题索引 讨论的内容以及它们的出版物类型和研究设计。索引器很难跟上 鉴于出版文献的爆炸式增长，手动作业。尽管 NLM 最近雇用了 自动机器学习方法根据讨论的主要主题索引文章，仍然没有 自动为所有出版物类型和研究设计中的每篇文章建立索引。我们最近有 创建了一个工作原型工具 Multi-Tagger，它为所有数据分配了概率预测分数 50 种不同出版物类型和研究设计的 PubMed 文章（统称为 PT）。我们现在建议 开发 Multi-Tagger 2.0，以处理更广泛的研究设计、文章、用户和用例，并 确保数据以适合每种不同类型用户的形式传播。具体来说， 我们的目标是： 目标 1. 优化为 PubMed 和非 PubMed 分配出版物类型和研究设计的方法 PubMed 生物医学文章、预印本和手稿。 目标 2. 详细评估 PT，同时考虑模型性能、用例和用户。 目标 3. 通过查询接口和 API 优化 PT 预测分数的传播。 目标 4. 探索如何将 Multi-Tagger 与其他工具集成以自动化证据合成。 拟议的研究将极大地增强跨多个相关文章和预印本的检索 数据库，并将对广泛的生物医学最终用户（临床医生、研究人员、学生和 期刊编辑）以及用户组，包括系统评审组、书目数据库管理员、 研究人类疾病临床前动物模型的人员以及开发新药物的制药公司 药物治疗。因此，改善生物医学文献的基础设施将间接影响人类 健康。