PERFORM Publications

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Title		Authors	PubMed ID
1	Metaphors in context and in isolation: Familiarity, aptness, concreteness, metaphoricity, and structure norms for 300 two-word expressions	Pissani L; de Almeida RG;	41491452 PSYCHOLOGY
2	Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A scoping review	Jafarpour H; Wu G; Cheligeer CK; Yan J; Xu Y; Southern DA; Eastwood CA; Zeng Y; Quan H;	41072367 ENCS
3	Automated abdominal aortic calcification and trabecular bone score independently predict incident fracture during routine osteoporosis screening	Gebre AK; Sim M; Gilani SZ; Saleem A; Smith C; Hans D; Reid S; Monchka BA; Kimelman D; Jozani MJ; Schousboe JT; Lewis JR; Leslie WD;	41071096 ENCS
4	MedCLIP-SAMv2: Towards universal text-driven medical image segmentation	Koleilat T; Asgariandehkordi H; Rivaz H; Xiao Y;	40779830 ENCS
5	Contextual variations in the effects of social withdrawal, peer exclusion, and friendship on growth curves of depressed affect in late childhood	Commisso M; Persram RP; Lopez LS; Bukowski WM;	40583455 CONCORDIA
6	Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism	Cheligeer C; Southern DA; Yan J; Wu G; Pan J; Lee S; Martin EA; Jafarpour H; Eastwood CA; Zeng Y; Quan H;	40105654 ENCS
7	Leveraging Personal Technologies in the Treatment of Schizophrenia Spectrum Disorders: Scoping Review	D' Arcey J; Torous J; Asuncion TR; Tackaberry-Giddens L; Zahid A; Ishak M; Foussias G; Kidd S;	39348196 PSYCHOLOGY
8	Context-induced renewal of passive but not active coping behaviours in the shock-probe defensive burying task	Alexa Brown	37095421 PSYCHOLOGY
9	A new circuit underlying the renewal of appetitive Pavlovian responses: Commentary on Brown and Chaudhri (2022)	Valyear MD; Britt JP;	36700576 CSBN
10	Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications	Luo Z; Amayri M; Fan W; Bouguila N;	36685642 ENCS
11	Learning processes in relapse to alcohol use: lessons from animal models	Valyear MD; LeCocq MR; Brown A; Villaruel FR; Segal D; Chaudhri N;	36264342 PSYCHOLOGY
12	Supplementary dataset of context-dependent conditioned responding to an alcohol-predictive cue in female and male rats	Segal D; Valyear MD; Chaudhri N;	35330738 PSYCHOLOGY
13	Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures	Bourouis S; Pawar Y; Bouguila N;	35009726 ENCS
14	Indeterminate and Enriched Propositions in Context Linger: Evidence From an Eye-Tracking False Memory Paradigm	Antal C; de Almeida RG;	34744914 PSYCHOLOGY
15	The role of context on responding to an alcohol-predictive cue in female and male rats	Segal D; Valyear MD; Chaudhri N;	34742865 PSYCHOLOGY
16	Depressive Symptoms and Social Context Modulate Oxytocin's Effect on Negative Memory Recall	Wong SF; Cardoso C; Orlando MA; Brown CA; Ellenbogen MA;	34100542 PSYCHOLOGY
17	Filtration for improving surface water quality of a eutrophic lake.	Palakkeel Veetil D, Arriagada EC, Mulligan CN, Bhat S	33310244 ENCS
18	Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.	Ebadi A; Xi P; Tremblay S; Spencer B; Pall R; Wong A;	33230352 ENCS
19	The contribution of dry indoor built environment on the spread of Coronavirus: Data from various Indian states.	V AAR, R V, Haghighat F	32834934 ENCS
20	Comparing ABA, AAB, and ABC Renewal of Appetitive Pavlovian Conditioned Responding in Alcohol- and Sucrose-Trained Male Rats.	Khoo SY, Sciascia JM, Brown A, Chaudhri N	32116588 PSYCHOLOGY
21	Context controls the timing of responses to an alcohol-predictive conditioned stimulus.	Valyear MD, Chaudhri N	32017964 PSYCHOLOGY
22	Biodiversity Observations Miner: A web application to unlock primary biodiversity data from published literature.	Muñoz G, Kissling WD, van Loon EE	30692868 BIOLOGY

Title:	Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.
Authors:	Ebadi A, Xi P, Tremblay S, Spencer B, Pall R, Wong A
Link:	https://www.ncbi.nlm.nih.gov/pubmed/33230352
DOI:	10.1007/s11192-020-03744-7
Publication:	Scientometrics
Keywords:	COVID-19 research landscape; Machine learning; Structural topic modeling; Text mining; Topics evolution;
PMID:	33230352	Category:	Scientometrics	Date Added:	2020-11-25
Dept Affiliation:	ENCS 1 National Research Council Canada, Montréal, QC H3T 1J4 Canada. 2 Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC H3G 2W1 Canada. 3 National Research Council Canada, Ottawa, ON K1K 2E1 Canada. 4 National Research Council Canada, Fredericton, NB E3B 9W4 Canada. 5 Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3 Canada. 6 Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1 Canada. 7 Waterloo Artificial Intelligence Institute, Waterloo, ON N2L 3G1 Canada.

Description:

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.

Scientometrics. 2020 Nov 19; :1-15

Authors: Ebadi A, Xi P, Tremblay S, Spencer B, Pall R, Wong A

Abstract

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.

PMID: 33230352 [PubMed - as supplied by publisher]

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