GASTon: A Graph-Exploration System for Indexing, Annotating and

Visualizing PubMed Articles to Enhance the Analysis of Social

deTerminants of Health

Simone Bottoni

, Alberto Trombetta

, Flavio Bertini

, Danilo Montesi

, Francesca Bonin

Alessandra Pascale

, Martin Gleize

and Pierpaolo Tommasi

Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy

Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

IBM Research Europe - Dublin, Ireland

ﬂ

{fbonin, apascale, martin.gleize, ptommasi}@ie.ibm.com

Keywords:

Graph-Based Knowledge Visualisation, Graph-Exploration Tool, PubMed Knowledge Base, Social Determi-

nants of Health.

Abstract:

Many works have shown associations between social determinants of health (SDoH) –the social circumstances

in which people live– and health-related outcomes. However, the lack of SDoH data increases the challenges

in measuring and understanding their effect on people’s health and health systems. In this paper, we present

GASTon, a system for the indexing, annotation, and graph-based rendering of PubMed information to enable

the search and retrieval of SDoHs in scientiﬁc literature. Our work provides a way to associate speciﬁc

concepts with peer-reviewed articles to simplify the search for social factors. It builds a knowledge graph based

on PubMed publications and associates them with concepts extracted from the Uniﬁed Medical Language

System (UMLS) Metathesaurus. GASTon allows a full-text search and graph-based navigation and supports

an overview of the concepts and related publications. Moreover, the architecture allows scale-up thanks to its

containerized nature and parallelization capabilities. The system is open-source under the Apache V2 license.

1 INTRODUCTION

The World Health Organisation (WHO) deﬁnes the

Social Determinants of Health (SDoH) as the circum-

stances in which people grow, live, and work that af-

fect their health (Wilkinson et al., 2003). Examples

of SDoH include socioeconomic status, education, or

transportation, and addressing these is important to

improve an individual’s health and reduce disparities

across communities (Artiga and Hinton, 2018). Pre-

vious works have shown associations between SDoH

and health-related outcomes, such as wealth linked to

risk of hospital admissions or costs (Artiga and Hin-

ton, 2018)(Meddings et al., 2017). The global SARS-

COV-2 pandemic has further revealed the stark in-

equity and inequality in healthcare provision and re-

sources, often associated with different dimensions of

SDoH such as race, income, education level, or job

security (Bettencourt-Silva et al., 2020). Therefore,

timely and effective interventions that address social

and healthcare needs are now more critical than ever.

In recent years, there has been a growing number

of initiatives that tackle SDoH, including nutritional

programs, addressing food insecurity (i.e., availability

and access to healthy foods), transportation programs

boosting access to employment, or housing interven-

tions tackling homelessness issue (Artiga and Hinton,

2018). However, more effort is needed to provide

combined or coordinated approaches. On one side,

it is essential to measure the impact of SDoH across

the health continuum. On the other, there is a need to

identify gaps and inconsistencies in data, especially

since electronic health record systems have not tradi-

tionally been designed to capture SDoH-related data.

Furthermore, healthcare terminologies such as ICD-

10 or SNOMED-CT do not extensively or adequately

cover social concepts (Bettencourt-Silva et al., 2020).

Therefore, it is not straightforward to consistently col-

lect data about SDoH across datasets. Lack of SDoH

data is, in fact, one of the main challenges when it

424

Bottoni, S., Trombetta, A., Bertini, F., Montesi, D., Bonin, F., Pascale, A., Gleize, M. and Tommasi, P.

GASTon: A Graph-Exploration System for Indexing, Annotating and Visualizing PubMed Articles to Enhance the Analysis of Social deTerminants of Health.

DOI: 10.5220/0011714900003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 424-431

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

comes to measuring and understanding their effects.

Previous work has explored the connection be-

tween SDoH and health concepts based on pub-

lished literature (Park et al., 2021)(Gleize et al.,

2021)(Bettencourt-Silva et al., 2020)(Hatef et al.,

2019)(Meddings et al., 2017). However, the typical

bottleneck of these efforts lies in acquiring scientiﬁc

articles in a scalable, constant, and up-to-date manner,

as well as in being able to perform natural language

searches for SDoH related terms.

In this work, we present GASTon, a system that

provides a graph exploration rendering of the PubMed

knowledge base augmented with medical concept an-

notations. Speciﬁcally, GASTon consists of the fol-

lowing capabilities:

1. processes and indexes the whole PubMed

;

2. annotates the article with medical concepts

(UMLS concepts (Bodenreider, 2004));

3. updates the repository every day;

4. exposes an interface to allow searching and graph-

based rendering of data.

5. searches and identiﬁes SDoH in the processed

data by exploiting the association between articles

and UMLS concepts.

GASTon aims to facilitate access to published ev-

idence and improve the discovery of social deter-

minants of health factors within scientiﬁc articles.

Thanks to its containerized nature and parallelization

capabilities, it can easily scale up to handle heavier

workloads. The system has been made open source

under Apache V2 license

The paper is organized as follows. Section 2

presents an overview of the related works. Section 3

and Section 4 describe the dataset and the UMLS tax-

onomy used for annotation, respectively. In Section 5,

we present the overall workﬂow of GASTon, and in

Section 6 the detailed description of the architecture.

Finally, conclusions and future works are reported in

Section 7.

2 RELATED WORK

A few tools have been developed as PubMed deriva-

tives. Among these we mention: GoPubMed (Doms

and Schroeder, 2005), SemanticMedline (Kilicoglu

We refer here only to the publicly available informa-

tion provided by PubMed, i.e., abstract and available infor-

mation about authors, etc.

The system is open source and accessible for de-

ployment at the following links: https://github.com/

SimoneBottoni/PubMedKnowledgeGraph

Figure 1: Workﬂow of the system.

et al., 2007), MeshNet (Yang and Lee, 2018), and

MeSHy (Theodosiou et al., 2011).

GoPubMed (Doms and Schroeder, 2005) extracts

Gene Ontology (GO) (Harris et al., 2004) terms from

the abstracts of the publications in PubMed search re-

sult, and groups the publications according to the GO

terms. The users are given the PubMed search result

as a list in which the publications are categorized ac-

cording to the GO terms. Such a grouping can provide

the users with an easy way to identify publications

with research themes (or concepts), and the search can

be reﬁned using the sub-theme.

Semantic MEDLINE extracts predictions based

on UMLS concepts from the publications in the

PubMed search result (Kilicoglu et al., 2007). The list

of predictions is entered into the automatic summa-

rizer and then assorted into the list of semantic con-

densates (list of UMLS concepts), which is provided

to the users.

MeSHNet (Yang and Lee, 2018) is a prototype

application to explore the research trend of a re-

search area using a network graph. The authors ap-

ply a methodology to visualize a social network com-

posed of medical keywords in PubMed literature, so-

called MeSH terms; they select only the ”noteworthy”

MeSH terms from those extracted from the publica-

tions in a research area deﬁned by a search query and

represent them as a graph. The graph is handy for ex-

ploring research trends and under-investigated areas.

However, to our understanding, no information from

the original article is maintained in the graph.

MeSHy (Theodosiou et al., 2011) is speciﬁcally

designed to provide random knowledge domains that

might have implications for the users so that they can

explore novel and promising research areas. The sys-

tem calculated the rarity of MeSH terms associated

with an article.

GASTon annotates the articles with UMLS med-

ical concepts. In addition to previous work, it of-

fers a graph-based visualization of the articles and

the concepts with a free text search capability. Un-

like MeSH Net, GASTon uses UMLS concepts and

visualizes the complete information of the articles. In

addition, thanks to the annotation process with UMLS

concepts, Gaston provides experts, through full-text

search, a way to visualize the association between ar-

GASTon: A Graph-Exploration System for Indexing, Annotating and Visualizing PubMed Articles to Enhance the Analysis of Social

deTerminants of Health

425

Figure 2: Architecture of the system.

ticles and concepts that help them to ﬁnd new possi-

ble associations between social determinants of health

and health-related issues.

3 PubMed KNOWLEDGE BASE

PubMed is a free resource repository of biomedical

and life sciences literature. It has the purpose of im-

proving the search and retrieval of health-related in-

formation. The PubMed database includes more than

34 million citations and abstracts of biomedical liter-

ature.

PubMed comprises three primary sources, Med-

line, PubMed Central, and Bookshelf. Medline is a

bibliographic database containing references to arti-

cles from life science journals, newspapers, maga-

zines, and newsletters, concentrating on biomedicine;

it is the online version of MEDical Literature Analy-

sis and Retrieval System (MEDLARS). PubMed Cen-

tral (PMC) is an archive of biomedical and life science

full-text journal literature. The Bookshelf contains

citations, full-text books, and individual chapters re-

lated to biomedical, health, and life science.

PubMed is maintained by the National Center for

Biotechnology Information (NCBI), which publishes

a baseline annually that contains the whole PubMed

citation records (i.e., articles) in XML format. Ev-

ery day the NCBI also publishes updated ﬁles daily,

including new, revised, or deleted articles. Each

XML ﬁle contains articles with a speciﬁc structure

described in the PubMed Document Type Deﬁnition

(DTD). The article information includes the id, called

PubMed ID (PMID), the title, the abstract, the journal,

and the list of authors. It also contains information re-

garding an article’s history in the PubMed repository,

such as the date the record was created and the last

date the article was revised. PubMed does not include

full-text articles, but links to the full-text are usually

present.

4 UNIFIED MEDICAL

LANGUAGE SYSTEM (UMLS)

The UMLS is a set of ﬁles and software that com-

bines many health and biomedical vocabularies and

standards to create more effective and interoperable

biomedical information systems and services (Boden-

reider, 2004)(McCray et al., 2001). It includes differ-

ent knowledge sources, such as a Metathesaurus, a set

of hierarchies, deﬁnitions, and other relationships and

attributes.

UMLS Metathesaurus (Schuyler et al., 1993) is

a large, multi-lingual biomedical thesaurus organized

by concepts that contain information about biomedi-

cal and health-related concepts, their various names

and relationships. It is organized by concept or

meaning, connecting different names over a unique

and permanent Concept Unique Identiﬁer (CUI).

The UMLS Metathesaurus is used by different tools

to extract speciﬁc terminologies or ontologies from

text. One of the most used is MetaMap (Aronson,

2006)(Aronson and Lang, 2010) and its lite versions

MetaMapLite, a customizable and faster version of

MetaMap (Demner-Fushman et al., 2017). These

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426

Figure 3: Web UI. Example of the Full-Text search view.

named entity recognition tools use NLP techniques

to map biomedical text to the UMLS Metathesaurus

concepts.

5 GASTon WORKFLOW

The primary purpose of our tool is to provide an easier

and faster way to explore SDoHs in clinical literature.

We ﬁrst need to index the PubMed repository and en-

sure that the index stays updated daily. Then we need

to expose all the available information externally and

allow to query them in a simple way.

This section provides a high-level overview of

how PubMed data are indexed, processed, and anno-

tated. The workﬂow and its main phases are shown in

Figure 1. GASTon downloads the PubMed XML ﬁles

from the baseline repository and processes a user-

deﬁned amount of ﬁles in parallel. The process work-

ﬂow consists of three phases: parsing, annotating, and

collecting/indexing.

In the Parsing phase, an XML parser extracts all

the available information from an XML PubMed ﬁle,

converting them into a more convenient format for

GASTon. Then it starts the annotation phase, analyz-

ing the text (title and abstract) of the extracted articles

and associating them with speciﬁc UMLS Metathe-

saurus concepts that describe their speciﬁc character-

istics. The parsed and annotated PubMed baseline

data is stored in a relational database. To improve

the system’s performance, an index –optimized for

textual data– is built upon a subset of interest (e.g.,

we currently focus on data regarding authors, articles,

and concepts rather than on the history of articles).

Every day updated information on articles is re-

leased. GASTon looking for updates on the articles

and processing them through the previously described

phases. We store all the updated information in the

database incrementally, keeping track of history.

6 GASTon ARCHITECTURE

In Figure 2 we present the GASTon architecture. It

has three main components: a Manager that orches-

trates the interactions, a set of workers that concur-

rently processes the PubMed ﬁles, and a Web User

Interface (UI) to query and visualize the data.

6.1 Manager

The core component of our system is the Manager. It

is written using the Spring Boot framework (spring

projects, 2018)(Walls, 2015), and it has the purpose

of managing the system. In particular, it initializes

the system, manages the analysis of the PubMed data,

GASTon: A Graph-Exploration System for Indexing, Annotating and Visualizing PubMed Articles to Enhance the Analysis of Social

deTerminants of Health

427

Figure 4: Web UI. Example of the Full-Text search view details.

generates the index, and answers the users’ requests.

System Initialization. The Manager creates, if they

do not exist, a PostgreSQL relational database and

two RabbitMQ messaging queues for communica-

tions with the Workers (a requests queue and a replies

queue).

Job Orchestration. After the initialization, the

Manager distributes the baseline PubMed ﬁles, to be

processed by the workers through a remote partition-

ing job (spring projects, 2008)(Lui et al., 2011) called

BaseLine job. The Manager keeps track of the Base-

Line job progress, and once completed, it enables a

second remote partitioning job, the Update job. Every

day, at a given user-deﬁned time, the Manager awakes

to handle the Update job to elaborate on the daily up-

dates released by PubMed.

Index Maintenance. The Manager keeps an up-

dated index of the most relevant data. This in-

dex is built using Hibernate Search (hibernate,

2010)(Bernard and Grifﬁn, 2008) and Apache

Lucene (Białecki et al., 2012).

Fulﬁlling Users’ Queries. The Manager also han-

dles queries coming from web clients. A set of REST-

ful Application Programming Interface (API)s are ex-

posed to fulﬁl the requests.

6.2 Worker(s)

GASTon allows starting a user-deﬁned number of

workers. Each worker processes a subset of the

PubMed repository concurrently for higher through-

put. Each worker reads a message from the request

queue that points to a speciﬁc PubMed ﬁle and pro-

cesses it. The process consists of the parsing, annota-

tion, and collection phases described in Section 5.

During the parsing phase, a worker parses the in-

formation regarding the articles using Java Sax. The

articles’ data extracted by the parser, are sent, in the

annotation phase, by a worker to the MetaMapLite’s

APIs (Demner-Fushman et al., 2017) to generate a list

of keywords for each article. A worker passes as in-

put to the APIs the title and the abstract of each article

in the form of raw text. Then, MetaMapLite’s APIs

matches the input text with the UMLS Metathesaurus

and identiﬁes a list of relevant concepts. Each con-

cept is retrieved in the MetaMap Indexing (MMI) for-

mat and includes the CUI that is the keyword itself,

the preferred term that is the keyword correspond-

ing name in natural language, a score, and trigger in-

formation. The score represents the relevance of the

UMLS concept according to the MMI ranking func-

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Figure 5: Web UI. Example of an explorable 3D graph view.

tion (Aronson, 1997); instead, the trigger includes the

input text word that triggered the keyword and its

location in the input text. Finally, in the collection

phase, a worker stores the extracted articles’ data and

the keywords in the database.

Once a worker processes a ﬁle, it notiﬁes the Man-

ager with a message in the replies queue containing

the status of the workﬂow.

6.3 Web UI

We developed a modern multi-page React web appli-

cation that exploits the REST APIs provided by the

Manager to provide a convenient and easy way to ac-

cess and query the processed PubMed data. Our web

UI comprises two tools. The ﬁrst is a full-text search

that allows one to search between the indexed data;

the second is a 3D Graph that explores the relation-

ships among concepts and articles.

Full-Text Search Tool. We show the Full-Text

search tool in Figure 3. The Full-Text Search enables

scanning the processed PubMed repository to ﬁnd de-

tailed information that matches a speciﬁc topic. The

tool allows users to insert a query using natural lan-

guage and output a list of articles that match the query.

Each result represents a single article, showing essen-

tial information such as the title, the author(s), the

journal, the abstract, and the list of matched UMLS

concepts. Each article has a detailed view, shown in

Figure 4, that includes all the available information

in the PubMed repository regarding the article. The

detailed view includes the basic information, the list

of references, links where it is possible to ﬁnd the ar-

ticle, and the article’s history in the PubMed reposi-

tory. The detailed view also includes the complete list

of UMLS concepts associated with the article, with

the score and triggers, as presented in Section 6.2.

Through this concepts list and the score related to

each concept, it is possible to identify social deter-

minants of health. This association can allow experts

to discover new relations between social determinants

of health and health issues.

3 D Graph Tool. The 3D exploration graph tool,

shown in Figure 5, provides a graphical overview of

the concepts, the relationships among them, and the

related publications. This tool helps understand the

connections between different articles and the UMLS

concepts, with additional explanations about discov-

ered relationships or information about concepts.

The tool allows searching data by PMID, CUI, or

UMLS concept names. The result list is visualized

as a 3D graph. For the graph visualization, we used

react-force-graph, a React library (Asturiano, 2018)

displaying 3-dimensional space graph data structure

rendered using a force-directed iterative layout.

Each node represents a different type of entity, dis-

GASTon: A Graph-Exploration System for Indexing, Annotating and Visualizing PubMed Articles to Enhance the Analysis of Social

deTerminants of Health

429

Figure 6: Web UI. Example of an explorable 3D graph view details.

tinguishable by colour. It is possible to ﬁnd nodes

representing articles, authors, journals, or UMLS con-

cepts. The centre of the graph is always the searched

node. Every node has a detailed view, shown in Fig-

ure 6, where it is possible to ﬁnd other data regards the

node. Edges also have a detailed view that displays in-

formation regards the relationship between the nodes

they connect.

7 CONCLUSIONS AND FUTURE

WORK

In this paper, we presented a novel system for the

indexing, annotation, and graph-based rendering of

PubMed articles that facilitates the retrieval of SDoH

from PubMed articles. Our work provides a way to

associate speciﬁc UMLS Metathesaurus concepts to

the PubMed scientiﬁc literature to simplify the search

for social factors and their possible links to health is-

sues. The solution consists of a pipeline that down-

loads and parses PubMed articles’ records and then

annotates them using MetaMap UMLS concepts. In

addition, we provide a set of accessible APIs to query

the processed data and a modern multi-page web ap-

plication to search and visualize articles information.

Speciﬁcally, we developed a Full-Text Search inter-

face to search articles by keyword(s) and an interac-

tive 3D graph tool that allows us to have an overview

of the concepts and the related publications and to un-

derstand the relationships between different articles

and the UMLS concepts.

Further analysis can be conducted to improve the

search and visualization tools, integrating new ways

and functionalities to visualize and explore data (e.g.,

a dynamic list of relevant ﬁelds). This analysis must

be conducted with medical experts that can provide

solid feedback regards the accuracy and the utility of

the retrieved results and the beneﬁt of the proposed

tools that allow to visualization of that results. These

improvements may give users a better and more ac-

cessible overview of the relationships between arti-

cles and concepts to help identify social factors that

can impact people’s health.

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