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Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling.
PLoS One
September 2024
Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.
Article Synopsis
- The study focused on analyzing narratives from breast cancer patients to identify various concerns using a natural language processing model called BERT.
- Researchers labeled interview transcripts with five categories of concerns: "treatment," "physical," "psychological," "work/financial," and "family/friends."
- The classifiers created from these labeled texts demonstrated varying precision levels, with high scores for "physical" and "work/financial," and lower scores for "treatment," indicating room for improvement through domain adaptation techniques.
BERT4FCA: A method for bipartite link prediction using formal concept analysis and BERT.
PLoS One
June 2024
Graduate School of Informatics, Kyoto University, Sakyo Ward, Kyoto City, Kyoto, Japan.
Link prediction in bipartite networks finds practical applications in various domains, including friend recommendation in social networks and chemical reaction prediction in metabolic networks. Recent studies have highlighted the potential for link prediction by maximal bi-cliques, which is a structural feature within bipartite networks that can be extracted using formal concept analysis (FCA). Although previous FCA-based methods for bipartite link prediction have achieved good performance, they still have the problem that they cannot fully capture the information of maximal bi-cliques.
View Article and Find Full Text PDFDiffering Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer.
JMIR Cancer
May 2024
Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
Background: Breast cancer affects the lives of not only those diagnosed but also the people around them. Many of those affected share their experiences on social media. However, these narratives may differ according to who the poster is and what their relationship with the patient is; a patient posting about their experiences may post different content from someone whose friends or family has breast cancer.
View Article and Find Full Text PDFRisk perception, knowledge about SARS-CoV-2, and perception towards preventive measures in Italy: a nationwide cross-sectional study.
J Prev Med Hyg
March 2023
Department of Public Health Sciences, University of Turin, Turin, Italy.
Introduction: After COVID-19 outbreak, governments adopted several containment measures. Risk perception and knowledge may play a crucial role since they can affect compliance with preventive measures. This study aimed to explore the extent and the associated factors of risk perception, knowledge regarding SARS-CoV2, and perception towards preventive measures among the Italian population.
View Article and Find Full Text PDFFrom Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election.
Heliyon
May 2023
Institute of Software Development and Engineering, Innopolis University, Innopolis, Russia.
Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections.
Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset.
Methods: Two million tweets with 18 features were collected from Twitter containing public and personal tweets of the three top contestants - Atiku Abubakar, Peter Obi and Bola Tinubu - in the forthcoming 2023 Presidential election.
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