Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

Reddy, Sandeep; Bhaskar, Ravi; Padmanabhan, Sandosh; Verspoor, Karin; Mamillapalli, Chaitanya; Lahoti, Rani; Makinen, Ville-Petteri; Pradhan, Smitan; Kushwah, Puru; Sinha, Saumya

doi:10.1016/j.cmpbup.2021.100010

Cited by 12 publications

(10 citation statements)

References 17 publications

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“…The emergence of the COVID-19 pandemic has resulted in several studies and papers outlining the utility of AI in tackling various aspects of the disease like diagnosis, treatment, and surveillance [16][17][18][19] The number of AI papers published either as pre-print or peerreviewed has been unprecedented, even leading to the development of AI applications to keep up with and summarise the findings for scientists [20]. Some recent reviews have outlined how most of these studies or the AI applications presented in these studies have shown minimal value for clinical care [7,21].…”

Section: Introductionmentioning

confidence: 99%

Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care

Casey

Ansari

Nakisa

et al. 2023

Preprint

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Background: Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in healthcare environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. Objective: We have previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to healthcare environments. In this study, we apply the TEHAI to COVID-19 literature in order to assess how well translational topics are covered. Methods: A systematic literature search for COVID-AI studies published between December 2019-2020 resulted in 3,830 records. A subset of 102 papers that passed inclusion criteria were sampled for full review. Nine reviewers assessed the papers for translational value and collected descriptive data (each study was assessed by two reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. Results: We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, non-maleficence and service adoption received failed scores in most studies. Conclusions: Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 healthcare environments.

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Section: Introductionmentioning

confidence: 99%

Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care

Casey

Ansari

Nakisa

et al. 2023

Preprint

Self Cite

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“…Text mining facilitates information extraction, categorisation, grouping, trend analysis and visualisation 7 . The goal is to focus information searches to remove noise 8 and to identify hidden knowledge in the literature 9 . Zengul et al used text mining to classify literature from the NIH COVID-19 Portfolio.…”

Section: Introductionmentioning

confidence: 99%

“…developed a biomedical platform to collect data on COVID-19 clinical risks. The use of this platform revealed the difficulty of extracting relevant clinical information through text mining and the need for feedback from experts in the field to obtain reliable results 9 .…”

Section: Introductionmentioning

confidence: 99%

Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study

Guauque-Olarte,

Cifuentes-C,

Fong

2023

Sci Rep

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Text mining enables search, extraction, categorisation and information visualisation. This study aimed to identify oral manifestations in patients with COVID-19 using text mining to facilitate extracting relevant clinical information from a large set of publications. A list of publications from the open-access COVID-19 Open Research Dataset was downloaded using keywords related to oral health and dentistry. A total of 694,366 documents were retrieved. Filtering the articles using text mining yielded 1,554 oral health/dentistry papers. The list of articles was classified into five topics after applying a Latent Dirichlet Allocation (LDA) model. This classification was compared to the author's classification which yielded 17 categories. After a full-text review of articles in the category “Oral manifestations in patients with COVID-19”, eight papers were selected to extract data. The most frequent oral manifestations were xerostomia (n = 405, 17.8%) and mouth pain or swelling (n = 289, 12.7%). These oral manifestations in patients with COVID-19 must be considered with other symptoms to diminish the risk of dentist-patient infection.

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“…Teir experiments yielded an accuracy of 93% to 95%, which was higher than that of other techniques used in the study. In another study, Reddy et al [22] applied an NLP algorithm to segment a collection of medical publications. Te methodology involved segmenting all the words and the associated information.…”

Section: Introductionmentioning

confidence: 99%

News Classification and Categorization with Smart Function Sentiment Analysis

Nkongolo Wa Nkongolo

2023

International Journal of Intelligent Systems

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Search engines are tools used to find information on the Internet. Since the web has a plethora of websites, the engine queries the majority of active sites and builds a database organized according to keywords utilized in the search. Because of this, when a user types a few descriptive words on the home page of the search engine, the search function lists websites corresponding to these keywords. However, there are some problems with this search approach. For instance, if a user wants information about the word Jaguar, most search results are animals and cars. This is a polysemic problem that forces search engines to always provide the most popular but not the most relevant results. This article presents a study of using sentiment technology to help news classification and categorization and improve the classification accuracy. We have introduced a smart search function embedded into a search engine to tackle polysemic issues and record relevant results to determine their sentimentality. Therefore, this study presents a topic that involves several aspects of natural language processing (NLP) and sentiment analysis for news categorization and classification. A web crawler was used to collect British Broadcasting Corporation (BBC) news across the Internet, carried out preprocessing of text by using NLP, and applied sentiment analysis methods to determine the polarity of the processed text data. The sentimentality represents negative, positive, or neutral polarities assigned by the sentiment analysis algorithms. The research utilized the BBC news site to collect different information using a web crawler and a database to explore the sentimentality of BBC news. The natural language toolkit (NLTK) and BM25 indexed and preprocessed patterns in the database. The experimental results depict the proposed search function surpassing normal search with an accuracy rate of 85%. Moreover, the results show a negative polarity of BBC news using the Sentistrength algorithm. Furthermore, the Valence Aware Dictionary and sEntiment Reasoner (VADER) was the best-performing sentiment analysis model for news classification. This model obtained an accuracy of 85% using data collected with the proposed smart function.

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Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

Cited by 12 publications

References 17 publications

Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care

Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care

Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study

News Classification and Categorization with Smart Function Sentiment Analysis

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