A Bibliographic Dataset of Health Artificial Intelligence Research-Reference-Cited by-同舟云学术

A Bibliographic Dataset of Health Artificial Intelligence Research

Published:2024-01 Issue: Volume:4 Page:
ISSN:2765-8783
Container-title:Health Data Science
language:en
Short-container-title:Health Data Sci

Author:

Shi Xuanyu¹²^ORCID,Yin Daoxin²³,Bai Yongmei¹²,Zhao Wenjing¹²,Guo Xin⁴,Sun Huage²⁵,Cui Dongliang⁶,Du Jian¹²

Affiliation:

1. Institute of Medical Technology, Peking University, Beijing, China.

2. National Institute of Health Data Science for Huage Sun, Peking University, Beijing, China.

3. Advanced Institute of Information Technology, Peking University, Hangzhou, China.

4. Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.

5. Institute of Health Informatics, University College London, London, UK.

6. University of Science and Technology Beijing, Beijing, China.

Abstract

Objective: The aim of this study is to construct a curated bibliographic dataset for a landscape analysis on Health Artificial Intelligence (HAI) research. Data Source: We integrated HAI-related bibliographic records, including publications, open research datasets, patents, research grants, and clinical trials from Medline and Dimensions. Methods: Searching: Relevant documents were identified using Medical Subject Headings (MeSH) and Field of Research (FoR) indexed by 2 bibliographic databases, Medline and Dimensions. Extracting: MeSH terms annotated from the aforementioned bibliographic databases served as the primary information for our processing. For document records lacking MeSH terms, we re-extracted them using the Medical Text Indexer (MTI). Mapping: In order to enhance interoperability, HAI multi-documents were organized using a mapping system incorporating MeSH, FoR, The International Classification of Diseases (ICD-10), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). Integrating: All documents were curated based on a pre-defined ontology of health problems and AI technologies from the MeSH hierarchy. Results: We collected 96,332 HAI documents (publications: 75,820, open research datasets: 638, patents: 11,226, grants: 6,113, and clinical trials: 2,535) during 2009 to 2021. On average, 75.12% of the documents were tagged with at least one label related to either health problems or AI technologies (with 92.9% of publications tagged). Summary: This study presents a comprehensive pipeline for processing and curating HAI bibliographic documents following the FAIR (Findable, Accessible, Interoperable, Reusable) standard, offering a valuable multidimensional collection for the community. This dataset serves as a crucial resource for horizontally scanning the funding, research, clinical assessments, and innovations within the HAI field.

Publisher

American Association for the Advancement of Science (AAAS)

Link

https://spj.science.org/doi/pdf/10.34133/hds.0125

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