Table 1.

Comparison of studies that apply NLP to various applications related to COVID-19.

Study Reference	Application	Employed NLP Model	Data Source
[30]	EHRs	-BERT -Specific phenotypes associated with COVID-19 using the list of 60 regular expressions (NLP RegExp) -All signs, symptoms, and comorbidities were extracted with the quickUMLS algorithm [97] (NLP UMLS).	A multi-center study involving data from 39 hospitals
[98]	EHRs	Keyword-extraction NLP that uses an unsupervised ML approach (clustering)	450,114 patient CT comprehensive reports gathered from 1 January to October 2020
[99]	EHRs	Word frequency for text analytics and CNN trained using Word2vector as a classification model	Data are collected through telehealth visits, including 6813 patients, of whom 498 tested positive and 6315 tested negative
[32]	EHRs	NLP model (medical named entity recognition)	Audio or video recordings of clinic visits
[38]	EHRs	Multi-class logistic regression model trained n-gram features	The study cohort includes 1737 COVID-19 adult patients discharged from two hospitals in Boston, Massachusetts, between 10 March and 30 June 2022
[39]	EHRs	NLP rule-based pipeline	Data from VA Corporate Data Warehouse (CDW) include clinical data in 2020 between 1 January and 15 June
[33]	EHRs	Random-forest trained on N-grams	32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center
[34]	EHRs	NLP rule-based pipeline	6250 patients (5664 negative and 586 positives; 46,138 non-severe and 125 severe)
[36]	EHRs	BERT and Bi-LSTM with attention	Annotated 1472 clinical notes distinguishing COVID-19 diagnoses, testing, and symptoms
[35]	EHRs	NLP rule-based pipeline	NLP is validated on several datasets; the main one is related to COVID-19 and contains 50 posts (1162 sentences) of related dialogues
[44]	Mental health	Supervised text classification used stochastic gradient descent linear classifier with L1 penalty TF-IDF grams with principal component analysis with k-NN used for unsupervised clustering. LDA is used in topic modeling.	Social media: Reddit Mental Health Dataset including posts from 826,961 unique users
[45]	Mental health	BERT (ft)	Social media: 1000 English tweets for training the model and 1 million tweets included in the analysis
[46]	Mental health	Sentiment analytic systems called CrystalFeel	Social media: Over 20 million COVID-19 tweets between 28 January and April 2020
[47]	Mental health	Key phrase extraction and sentiment score using lexicon-based technique	Social media: 47 million COVID-19- related comments extracted from Twitter, Facebook, and YouTube
[100]	Mental health	Bi-directional LSTM and a self-attention layer	Social media: The diagnosed group has approximately 900,000 tweets from several countries. The control group has approximately 14 million tweets from several countries
[48]	Mental health	Sentence-BERT (SBERT)	9090 English free-form texts from 1451 students between 1 February and 30 April 2020
[52]	Health behaviors	BERT	1.1 million COVID-19-related tweets from 181 counties in the US
[54]	Health behaviors	-t-Distributed Stochastic Neighbor Embedding -DistilBART -VADER for sentiment analysis -Google’s Universal Sentence Encoder	189,958,459 English COVID-19-related tweets COVID-19 between 17 March to 27 July 2020
[55]	Health behaviors	SVM, XGBoost, and LSTM	771,268 tweets from the US between January and October 2020
[56]	Health behaviors	LDA for topic modeling andaspect-based sentiment analysis	English COVID-19 tweets are 25,595 for Canada and 293,929 for the US
[57]	Health behaviors	BERT	2,349,659 tweets related to COVID-19 vaccination 1 month after the first vaccine announcement
[52]	Health behaviors	BERT	1.1 million COVID-19-related tweets from 181 counties in the US
[71]	Misinformation detection	Uses SAFE systems developed in [53]	2029 news articles on COVID-19 (between January and May 2020) and 140,820 tweets that disclose how these news articles have circulated on Twitter
[76]	Misinformation detection	NLP and network analysis method	4573 annotated tweets comprising 3629 users
[73]	Misinformation detection	SVM	10,700 social media posts and articles of real and fake news on COVID-19
[101]	Misinformation detection	Sentence-BERT and BERTScore	4800 expert-annotated social media posts
[77]	Misinformation detection	BERT and ALBERT	5500 claims and explanation pairs
[90]	COVID QA systems	BERT and LDA	COVID-19 scientific publications: CORD-19 dataset
[83]	COVID QA systems	T5	COVID-19 scientific publications: CORD-19 dataset
[102]	COVID QA systems	-An ensemble of two QA models (HLTC-MRQA and BioBERT) for the QA model -BART [88] for abstractive summarization -ALBERT [89] in extractive summarization block	COVID-19 scientific publications: CORD-19 dataset
[91]	COVID QA systems	BioBERT	COVID-19 scientific publications: CORD-19 dataset, with additional 111 QA pairs annotated for test
[92]	COVID QA systems	Synthetically generated QA examples to optimize the QA system performance on closed domains. The machine reading comprehension employs the Roberta model.	COVID-19 scientific publications: CORD-19 dataset
[95]	Knowledge transfer	XLM-R Large	Dataset, M-CID, containing 5271 utterances across English, Spanish, French, and Spanglish
[96]	Knowledge transfer	Multilingual Universal Sentence Encoder [103]	4,683,226 geo-referenced tweets in 60 languages located in Europe
[94]	Knowledge transfer	Variant transformers big architecture	The model is trained on more than 350 million sentences in French, Spanish, German, Italian, and Korean (into English)