PubMed^® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID^®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.

Background: Health equity is a multidimensional concept that has been internationally considered as an essential element for health system development. However, our understanding about the root causes of health equity is limited. In this study, we investigated the historical roots and seminal works of research on health equity.Methods: Health equity-related publications were identified and downloaded from the Web of Science database (n = 67,739, up to 31 October 2018). Their cited references (n = 2,521,782) were analyzed through Reference Publication Year Spectroscopy (RPYS), which detected the historical roots and important works on health equity and quantified their impact in terms of referencing frequency.Results: A total of 17 pronounced peaks and 31 seminal works were identified. The first publication on health equity appeared in 1966. But the first cited reference can be traced back to 1801. Most seminal works were conducted by researchers from the US (19, 61.3%), the UK (7, 22.6%) and the Netherlands (3, 9.7%). Research on health equity experienced three important historical stages: origins (1800–1965), formative (1966–1991) and development and expansion (1991–2018). The ideology of health equity was endorsed by the international society through the World Health Organization (1946) declaration based on the foundational works of Chadwick (1842), Engels (1945), Durkheim (1897) and Du Bois (1899). The concept of health equity originated from the disciplines of public health, sociology and political economics and has been a major research area of social epidemiology since the early nineteenth century. Studies on health equity evolved from evidence gathering to the identification of cost-effective policies and governmental interventions.Conclusion: The development of research on health equity is shaped by multiple disciplines, which has contributed to the emergence of a new stream of social epidemiology and political epidemiology. Past studies must be interpreted in light of their historical contexts. Further studies are needed to explore the causal pathways between the social determinants of health and health inequalities.

Background: Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research. Objective: The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution. Methods: In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P1), Promising Index (P2), Prestige Index (P3), and Collaboration Index (CI). Results: We found that the maxima of P1, P3, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P1 and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P2 was more sensitive to immediate changes. P3 reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug. Conclusions: In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.

To preserve the urban fabric or characteristics in specific quarters, there is often a need to either strengthen or lessen the homogeneity of the urban fabric when inserting new buildings. Evaluating the form of urban fabric is fundamentally important for urban design practice and relevant policy making. However, the quantitative methods and attempts are rare due to the lack of available methods. To address this deficiency, this article presents a GIS-based method to measure the homogeneity of urban fabric by extracting attributes directly from the geometry of 2D building footprints, including the angles between buildings, areas of building footprints, and distances between buildings. These attributes are calculated for separate overlaid grids in the open space between buildings, where each grid holds the measured values for one attribute. We test the method on a prototype, which we applied on four real sites using OpenStreetMap data. The results show how to categorize different kinds of urban fabric based on the new measure of homogeneity. The method can be used to interactively inform urban planners how new design proposals would affect the homogeneity of a neighborhood. Furthermore, the measure can be used to synthesize new design variants with a defined homogeneity.