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.

项目总结/文摘