Ideas for how informaticians can get involved with COVID-19 research

Moore, Jason H.; Barnett, Ian; Boland, Mary Regina; Chen, Yong; Demiris, George; Gonzalez-Hernandez, Graciela; Herman, Daniel S.; Himes, Blanca E.; Hubbard, Rebecca A.; Kim, Dokyoon; Morris, Jeffrey S.; Mowery, Danielle L.; Ritchie, Marylyn D.; Shen, Li; Urbanowicz, Ryan J.; Holmes, John H.

doi:10.1186/s13040-020-00213-y

Cited by 22 publications

(25 citation statements)

References 48 publications

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“…These may span from prediction of disease burden to global efforts on finding vaccinations. Digital health strategies should identify potential application areas for research and support health informaticians to achieve the best outcomes and benefits (25) . The limitation of this study is that this was based on a rapid review method, and therefore a certain degree of bias is observed.…”

Section: Review Articlementioning

confidence: 99%

A rapid review of digital health strategies and policies in response to COVID-19 pandemic

Sylva

Brown

Batchelor

2020

Sri Lanka J Bio-Medical Informatics

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Section: Review Articlementioning

confidence: 99%

A rapid review of digital health strategies and policies in response to COVID-19 pandemic

Sylva

Brown

Batchelor

2020

Sri Lanka J Bio-Medical Informatics

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“…As a matter of fact, as also reported by Moore et al . [ 2 ], EHR and health information systems have a 2-fold nature, as they must provide flexible and robust point-of-care solutions and at the same time they can feed clinical research with invaluable real-world data.…”

Section: Introductionmentioning

confidence: 99%

Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

Dagliati

Malovini

Tibollo

et al. 2021

Briefings in Bioinformatics

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The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

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“…Scientists are developing new methods for determining infection rates, disease burden, treatment efficacy, and patient outcomes [ 1 ]. Our ability to detect and phenotype patients with COVID-19 and controls for clinical and translational studies requires clinical symptomatology, radiological imaging, laboratory tests, and associated disorders derived from electronic health record data [ 2 ]. Much of this information can be locked within the electronic health record clinical notes [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation

Parikh¹,

Davoudi²,

Yu³

et al. 2021

JMIR Med Inform

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Background Scientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented COVID-19–related symptoms, findings, and disorders from clinical text sources in an electronic health record. Word embeddings can identify terms related to these clinical concepts from both the biomedical and nonbiomedical domains, and are being shared with the open-source community at large. However, it’s unclear how useful openly available word embeddings are for developing lexicons for COVID-19–related concepts. Objective Given an initial lexicon of COVID-19–related terms, this study aims to characterize the returned terms by similarity across various open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to the word embedding source. Methods We compared seven openly available word embedding sources. Using a series of COVID-19–related terms for associated symptoms, findings, and disorders, we conducted an interannotator agreement study to determine how accurately the most similar returned terms could be classified according to semantic types by three annotators. We conducted a qualitative study of COVID-19 queried terms and their returned terms to detect informative patterns for constructing lexicons. We demonstrated the utility of applying such learned synonyms to discharge summaries by reporting the proportion of patients identified by concept among three patient cohorts: pneumonia (n=6410), acute respiratory distress syndrome (n=8647), and COVID-19 (n=2397). Results We observed high pairwise interannotator agreement (Cohen kappa) for symptoms (0.86-0.99), findings (0.93-0.99), and disorders (0.93-0.99). Word embedding sources generated based on characters tend to return more synonyms (mean count of 7.2 synonyms) compared to token-based embedding sources (mean counts range from 2.0 to 3.4). Word embedding sources queried using a qualifier term (eg, dry cough or muscle pain) more often returned qualifiers of the similar semantic type (eg, “dry” returns consistency qualifiers like “wet” and “runny”) compared to a single term (eg, cough or pain) queries. A higher proportion of patients had documented fever (0.61-0.84), cough (0.41-0.55), shortness of breath (0.40-0.59), and hypoxia (0.51-0.56) retrieved than other clinical features. Terms for dry cough returned a higher proportion of patients with COVID-19 (0.07) than the pneumonia (0.05) and acute respiratory distress syndrome (0.03) populations. Conclusions Word embeddings are valuable technology for learning related terms, including synonyms. When leveraging openly available word embedding sources, choices made for the construction of the word embeddings can significantly influence the words learned.

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Ideas for how informaticians can get involved with COVID-19 research

Cited by 22 publications

References 48 publications

A rapid review of digital health strategies and policies in response to COVID-19 pandemic

A rapid review of digital health strategies and policies in response to COVID-19 pandemic

Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation

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