Novel informatics approaches to COVID-19 Research: From methods to applications

Xu, Hua; Buckeridge, David L.; Wang, Fei; Tarczy‐Hornoch, Peter

doi:10.1016/j.jbi.2022.104028

Cited by 7 publications

(7 citation statements)

References 47 publications

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“…They used NGS with large gene panels of somatic cancer mutations of circulating tumor DNA (ctDNA). In parallel, Xu et al [66] pointed to the application of this area on the current COVID-19 pandemic research, in which TBI have helped understand potential disease mechanisms, and effective and less toxic treatments.…”

Section: Application Of Tbi In Complex Diseasesmentioning

confidence: 99%

Translational Bioinformatics Applied to the Study of Complex Diseases

Casotti

Meira

Alves

et al. 2023

Genes

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Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: “How does TBI provide a scientific understanding of complex diseases?” An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.

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Section: Application Of Tbi In Complex Diseasesmentioning

confidence: 99%

Translational Bioinformatics Applied to the Study of Complex Diseases

Casotti

Meira

Alves

et al. 2023

Genes

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“…We define 7 entity types based on public health research needs (Tsao et al, 2021;Xu et al, 2022), including 3 general entity types and 4 medical entity types for annotation. In particular, we select 4 entity types for additional sentiment annotation with 3 types of sentiment labels: positive, negative, and neutral.…”

Section: Data Annotation Processmentioning

confidence: 99%

“…Large-scale analyses of such tweets are vital to understanding the public's opinions towards medical topics, which can further help medical research or public health management. Therefore, many studies have been proposed to track and analyze people through social media platforms such as Twitter, one of the most widely used social media platforms (Banda et al, 2021;Shahi et al, 2021;Xue et al, 2020;Boon-Itt et al, 2020;Hua et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets

Zhou¹,

Wang²,

Chong³

et al. 2022

Preprint

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The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemicrelated entities (e.g., drugs, vaccines) on social media. However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective. This paper releases METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19-related tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization). To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19-related tweets. We benchmark the performance of classical machine learning models and state-of-the-art deep learning models on NER and TSA tasks with extensive experiments. Results show that the dataset has vast room for improvement for both NER and TSA tasks. With rich annotations and comprehensive benchmark results, we believe METS-CoV is a fundamental resource for building better medical social media understanding tools and facilitating computational social science research, especially on epidemiological topics. Our data, annotation guidelines, benchmark models, and source code are publicly available (https://github.com/YLab-Op en/METS-CoV) to ensure reproducibility.

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“… 3 The coronavirus disease 2019 (COVID-19) pandemic, for example, has highlighted the importance of using EHR data to uncover and monitor patterns in disease spread and severity. 4 , 5 In addition to the potential benefits of using EHR data for research, however, there are also challenges, including EHR data quality concerns, timely access, patient protections and confidentiality, and the ability to generalize results based on EHR data. 3 , 4 , 6 …”

Section: Introductionmentioning

confidence: 99%

Electronic health record data quality assessment and tools: a systematic review

Lewis

Weiskopf

Abrams

et al. 2023

Journal of the American Medical Informatics Association

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Objective We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies. Materials and Methods We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process. Results We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology. Discussion There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality. Conclusion Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.

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Novel informatics approaches to COVID-19 Research: From methods to applications

Cited by 7 publications

References 47 publications

Translational Bioinformatics Applied to the Study of Complex Diseases

Translational Bioinformatics Applied to the Study of Complex Diseases

METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets

Electronic health record data quality assessment and tools: a systematic review

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