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Article: Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.

Gyftopoulos, Sotirios / Drosatos, George / Fico, Giuseppe / Pecchia, Leandro / Kaldoudi, Eleni

Behavioral sciences (Basel, Switzerland)

2024 Volume 14, Issue 2

Abstract: The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The ... ...

Abstract	The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.
Language	English
Publishing date	2024-02-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651997-5
ISSN	2076-328X
ISSN	2076-328X
DOI	10.3390/bs14020128
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Blockchain Applications in the Biomedical Domain: A Scoping Review.

Drosatos, George / Kaldoudi, Eleni

Computational and structural biotechnology journal

2019 Volume 17, Page(s) 229–240

Abstract: Blockchain is a distributed, immutable ledger technology introduced as the enabling mechanism to support cryptocurrencies. Blockchain solutions are currently being proposed to address diverse problems in different domains. This paper presents a scoping ... ...

Abstract	Blockchain is a distributed, immutable ledger technology introduced as the enabling mechanism to support cryptocurrencies. Blockchain solutions are currently being proposed to address diverse problems in different domains. This paper presents a scoping review of the scientific literature to map the current research area of blockchain applications in the biomedical domain. The goal is to identify biomedical problems treated with blockchain technology, the level of maturity of respective approaches, types of biomedical data considered, blockchain features and functionalities exploited and blockchain technology frameworks used. The study follows the PRISMA-ScR methodology. Literature search was conducted on August 2018 and the systematic selection process identified 47 research articles for detailed study. Our findings show that the field is still in its infancy, with the majority of studies in the conceptual or architectural design phase; only one study reports real world demonstration and evaluation. Research is greatly focused on integration, integrity and access control of health records and related patient data. However, other diverse and interesting applications are emerging, addressing medical research, clinical trials, medicines supply chain, and medical insurance.
Language	English
Publishing date	2019-02-08
Publishing country	Netherlands
Document type	Journal Article ; Review
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2019.01.010
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study.

Geronikolou, Styliani / Drosatos, George / Chrousos, George

JMIR formative research

2021 Volume 5, Issue 9, Page(s) e27741

Abstract: Background: The effectiveness of public health measures depends upon a community's compliance as well as on its positive or negative emotions.: Objective: The purpose of this study was to perform an analysis of the expressed emotions in English ... ...

Abstract	Background: The effectiveness of public health measures depends upon a community's compliance as well as on its positive or negative emotions. Objective: The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. Methods: The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. Results: The most frequent emotion that was detected in the tweets was "surprise" at the emerging contagion, while the imposed isolation resulted mostly in "anger" (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked "flight or fight" or "fear versus anger" response to the contagion. Conclusions: The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance.
Language	English
Publishing date	2021-09-29
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/27741
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic.

Ntompras, Charalampos / Drosatos, George / Kaldoudi, Eleni

Journal of computational social science

2021 Volume 5, Issue 1, Page(s) 687–729

Abstract: The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large- ... ...

Abstract	The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large-scale, high-resolution, temporal, and geospatial content analysis of Twitter related discussions. Analysis considered 20,230,833 English language original COVID-19-related tweets with global origin retrieved between January 25, 2020 and April 30, 2020. Fine grain topic analysis identified 91 meaningful topics. Most of the topics showed a temporal evolution with local maxima, underlining the short-lived character of discussions in Twitter. When compared to real-world events, temporal popularity curves showed a good correlation with and quick response to real-world triggers. Geospatial analysis of topics showed that approximately 30% of original English language tweets were contributed by USA-based users, while overall more than 60% of the English language tweets were contributed by users from countries with an official language other than English. High-resolution temporal and geospatial analysis of Twitter content shows potential for political, economic, and social monitoring on a global and national level.
Language	English
Publishing date	2021-10-20
Publishing country	Singapore
Document type	Journal Article
ZDB-ID	2916161-7
ISSN	2432-2725 ; 2432-2717
ISSN (online)	2432-2725
ISSN	2432-2717
DOI	10.1007/s42001-021-00150-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A probabilistic semantic analysis of eHealth scientific literature.

Drosatos, George / Kaldoudi, Eleni

Journal of telemedicine and telecare

2019 Volume 26, Issue 7-8, Page(s) 414–432

Abstract: Introduction: eHealth emerged as an interdisciplinary research area about 70 years ago. This study employs probabilistic techniques to semantically analyse scientific literature related to the field of eHealth in order to identify topics and trends and ... ...

Abstract	Introduction: eHealth emerged as an interdisciplinary research area about 70 years ago. This study employs probabilistic techniques to semantically analyse scientific literature related to the field of eHealth in order to identify topics and trends and discuss their comparative evolution. Methods: Authors collected titles and abstracts of published literature on eHealth as indexed in PubMed. Basic statistical and bibliometric techniques were applied to overall describe the collected corpus; Latent Dirichlet Allocation was employed for unsupervised topics identification; topics trends analysis was performed, and correlation graphs were plotted were relevant. Results: A total of 30,425 records on eHealth were retrieved from PubMed (all records till 31 December 2017, search on 8 May 2018) and 23,988 of these were included to the study corpus. eHealth domain shows a growth higher than the growth of the entire PubMed corpus, with a mean increase of eHealth corpus proportion of about 7% per year for the last 20 years. Probabilistic topics modelling identified 100 meaningful topics, which were organised by the authors in nine different categories: general; service model; disease; medical specialty; behaviour and lifestyle; education; technology; evaluation; and regulatory issues. Discussion: Trends analysis shows a continuous shift in focus. Early emphasis on medical image transmission and system integration has been replaced by increased focus on standards, wearables and sensor devices, now giving way to mobile applications, social media and data analytics. Attention on disease is also shifting, from initial popularity of surgery, trauma and acute heart disease, to the emergence of chronic disease support, and the recent attention to cancer, infectious disease, mental disorders, paediatrics and perinatal care; most interestingly the current swift increase is in research related to lifestyle and behaviour change. The steady growth of all topics related to assessment and various systematic evaluation techniques indicates a maturing research field that moves towards real world application.
MeSH term(s)	Bibliometrics ; Chronic Disease ; Female ; Humans ; Mobile Applications/trends ; Pregnancy ; Semantics ; Telemedicine/trends ; Wearable Electronic Devices/trends
Language	English
Publishing date	2019-05-12
Publishing country	England
Document type	Journal Article
ZDB-ID	1340281-x
ISSN	1758-1109 ; 1357-633X
ISSN (online)	1758-1109
ISSN	1357-633X
DOI	10.1177/1357633X19846252
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Article: Blockchain Applications in the Biomedical Domain: A Scoping Review

Drosatos, George / Kaldoudi, Eleni

Computational and Structural Biotechnology Journal. 2019, v. 17

2019

Abstract	Blockchain is a distributed, immutable ledger technology introduced as the enabling mechanism to support cryptocurrencies. Blockchain solutions are currently being proposed to address diverse problems in different domains. This paper presents a scoping review of the scientific literature to map the current research area of blockchain applications in the biomedical domain. The goal is to identify biomedical problems treated with blockchain technology, the level of maturity of respective approaches, types of biomedical data considered, blockchain features and functionalities exploited and blockchain technology frameworks used. The study follows the PRISMA-ScR methodology. Literature search was conducted on August 2018 and the systematic selection process identified 47 research articles for detailed study. Our findings show that the field is still in its infancy, with the majority of studies in the conceptual or architectural design phase; only one study reports real world demonstration and evaluation. Research is greatly focused on integration, integrity and access control of health records and related patient data. However, other diverse and interesting applications are emerging, addressing medical research, clinical trials, medicines supply chain, and medical insurance.
Keywords	biomedical research ; biotechnology ; clinical trials ; health insurance ; patients ; supply chain
Language	English
Size	p. 229-240.
Publishing place	Elsevier B.V.
Document type	Article
ISSN	2001-0370
DOI	10.1016/j.csbj.2019.01.010
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Supporting topic modeling and trends analysis in biomedical literature.

Kavvadias, Spyridon / Drosatos, George / Kaldoudi, Eleni

Journal of biomedical informatics

2020 Volume 110, Page(s) 103574

Abstract: Topic modeling refers to a suite of probabilistic algorithms for extracting popular topics from a collection of documents. A common approach involves the use of the Latent Dirichlet Allocation (LDA) algorithm, and, although free implementations are ... ...

Abstract	Topic modeling refers to a suite of probabilistic algorithms for extracting popular topics from a collection of documents. A common approach involves the use of the Latent Dirichlet Allocation (LDA) algorithm, and, although free implementations are available, their deployment in general requires a certain degree of programming expertise. This paper presents a user-friendly web-based application, specifically designed for the biomedical professional, that supports the entire process of topic modeling and comparative trends analysis of scientific literature. The application was evaluated for its efficacy and usability by intended users with no programming expertise (15 biomedical professionals). Results of evaluation showed a positive acceptance of system functionalities and an overall usability score of 76/100 in the System Usability Score (SUS) scale. This suggests that literature topic modeling can become more popular amongst biomedical professionals via the use of a user-friendly application that fully supports the entire workflow, thus opening new perspectives for literature review and scientific research.
MeSH term(s)	Algorithms ; Publications
Language	English
Publishing date	2020-09-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2020.103574
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Current trends in cancer immunotherapy: a literature-mining analysis.

Pouliliou, Stamatia / Nikolaidis, Christos / Drosatos, George

Cancer immunology, immunotherapy : CII

2020 Volume 69, Issue 12, Page(s) 2425–2439

Abstract: Cancer immunotherapy is a rapidly growing field that is completely transforming oncology care. Mining this knowledge base for biomedically important information is becoming increasingly challenging, due to the expanding number of scientific publications, ...

Abstract	Cancer immunotherapy is a rapidly growing field that is completely transforming oncology care. Mining this knowledge base for biomedically important information is becoming increasingly challenging, due to the expanding number of scientific publications, and the dynamic evolution of this subject with time. In this study, we have employed a literature-mining approach that was used to analyze the cancer immunotherapy-related publications listed in PubMed and quantify emerging trends. A total of 93,033 publications published in 5055 journals have been retrieved, and 141 meaningful topics have been identified, which were further classified into eight distinct categories. Statistical analysis indicates a mean annual increase in the number of published papers of approximately 8% in the last 20 years. The research topics that exhibited the highest trends included "immune checkpoint inhibitors," "tumor microenvironment," "HPV vaccination," "CAR T-cells," and "gene mutations/tumor profiling." The top identified cancer types included "lung," "colorectal," and "breast cancer," and a shift in popularity from hematological to solid tumors was observed. As regards clinical research, a transition from early phase clinical trials to randomized control trials was recorded, indicating that the field is entering a more advanced phase of development. Overall, this mining approach provided an unbiased analysis of the cancer immunotherapy literature in a time-conserving and scale-efficient manner.
MeSH term(s)	Antineoplastic Agents, Immunological/therapeutic use ; Bibliometrics ; Cancer Vaccines/therapeutic use ; Data Mining ; Humans ; Immunotherapy/methods ; Immunotherapy/trends ; Mutation ; Neoplasms/genetics ; Neoplasms/immunology ; Neoplasms/therapy ; Papillomavirus Vaccines/therapeutic use ; PubMed/statistics & numerical data ; Randomized Controlled Trials as Topic
Chemical Substances	Antineoplastic Agents, Immunological ; Cancer Vaccines ; Papillomavirus Vaccines
Language	English
Publishing date	2020-06-15
Publishing country	Germany
Document type	Journal Article ; Systematic Review
ZDB-ID	195342-4
ISSN	1432-0851 ; 0340-7004
ISSN (online)	1432-0851
ISSN	0340-7004
DOI	10.1007/s00262-020-02630-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Capturing Provenance, Evolution and Modification of Clinical Protocols via a Heterogeneous, Semantic Social Network.

Portokallidis, Nick / Drosatos, George / Kaldoudi, Eleni

Studies in health technology and informatics

2016 Volume 225, Page(s) 592–596

Abstract: Healthcare delivery is largely based on medical best practices as in clinical protocols. Research so far has addressed the computerized execution of clinical protocols by developing a number of related representation languages, execution engines and ... ...

Abstract	Healthcare delivery is largely based on medical best practices as in clinical protocols. Research so far has addressed the computerized execution of clinical protocols by developing a number of related representation languages, execution engines and integrated platforms to support real time execution. However, much less effort has been put into organizing clinical protocols for use and reuse. In this paper we propose a heterogeneous semantic social network to describe and organize clinical protocols based on their provenance, evolution and modifications. The proposed approach allows semantic tagging and enrichment of clinical protocols so that they can be used and re-used across platforms and also be linked directly to other relevant scientific information, e.g. published works in PubMed or personal health records, and other clinical information systems.
MeSH term(s)	Clinical Protocols/classification ; Clinical Protocols/standards ; Greece ; Information Dissemination ; Medical Record Linkage/standards ; Semantics ; Social Media/standards ; Social Support ; Terminology as Topic ; Vocabulary, Controlled
Language	English
Publishing date	2016
Publishing country	Netherlands
Document type	Journal Article
ISSN	0926-9630
ISSN	0926-9630
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Blockchain-Based Notarization Service for Biomedical Knowledge Retrieval

Kleinaki, Athina-Styliani / Mytis-Gkometh, Petros / Drosatos, George / Efraimidis, Pavlos S / Kaldoudi, Eleni

Computational and Structural Biotechnology Journal. 2018, v. 16

2018

Abstract: Biomedical research and clinical decision depend increasingly on scientific evidence realized by a number of authoritative databases, mostly public and continually enriched via peer scientific contributions. Given the dynamic nature of biomedical ... ...

Abstract	Biomedical research and clinical decision depend increasingly on scientific evidence realized by a number of authoritative databases, mostly public and continually enriched via peer scientific contributions. Given the dynamic nature of biomedical evidence data and their usage in the sensitive domain of biomedical science, it is important to ensure retrieved data integrity and non-repudiation. In this work, we present a blockchain-based notarization service that uses smart digital contracts to seal a biomedical database query and the respective results. The goal is to ensure that retrieved data cannot be modified after retrieval and that the database cannot validly deny that the particular data has been provided as a result of a specific query. Biomedical evidence data versioning is also supported. The feasibility of the proposed notarization approach is demonstrated using a real blockchain infrastructure and is tested on two different biomedical evidence databases: a publicly available medical risk factor reference repository and on the PubMed database of biomedical literature references and abstracts.
Keywords	biomedical research ; databases ; infrastructure ; risk factors
Language	English
Size	p. 288-297.
Publishing place	Elsevier B.V.
Document type	Article
ISSN	2001-0370
DOI	10.1016/j.csbj.2018.08.002
Database	NAL-Catalogue (AGRICOLA)

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