NewsMeSH: A new classifier designed to annotate health news with MeSH headings

Costa, Joao Pita; Rei, Luis; Stopar, Luka; Fuart, Flavio; Grobelnik, Marko; Mladenić, Dunja; Novalija, Inna; Staines, Anthony; Pääkkönen, Jarmo; Konttila, Jenni; Bidaurrazaga, J.; Belar, Oihana; Henderson, Christine; Epelde, Gorka; Gabaráin, Mónica Arrúe; Carlin, Paul; Wallace, Jonathan

doi:10.1016/j.artmed.2021.102053

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…Also related to this work, and specifically focusing on the annotation of news articles with MeSH terms, in 2021, we developed a text classifier learning the MEDLINE records labeled by MeSH Headings [12]. In this work, we build on the latter-mentioned work to improve queries of different rare disease topics in a wide range of health documents, from news articles to medical reports.…”

Section: Related Workmentioning

confidence: 99%

“…Most of their references include abstracts, from which we create our instances, and their corresponding MeSH headings, which are the basis for our labels. MEDLINE/PubMed data are an attractive source for creating machine learning datasets, a practice that started with the popular Ohsummed dataset [19] and was at the core of NewsMeSH [12]. Given the list of rare disease MeSH terms we obtained in Section 2.2 and the 2023 PubMed baseline data, we created a dataset of abstracts labeled to indicate whether they refer to rare diseases, non-rare diseases, or something else.…”

Section: Datasetmentioning

confidence: 99%

“…To facilitate experiments by reducing their computational cost, we randomly sampled 24,000 records for each class and randomly assigned each to a training, validation, or test set. The number of samples in each class is summarized in To evaluate our classifier on news data, we augmented the original NewsMeSH [12] dataset of 100 expert annotated news articles with an additional 40 evenly split between four different rare diseases: Angelman, De Lange, Fragile X, Kleefstra [15]. Each news article is expert-annotated with MeSH headings.…”

Section: Datasetmentioning

confidence: 99%

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Automatic Classification and Visualization of Text Data on Rare Diseases

Rei,

Pita Costa,

Zdolšek Draksler

2024

JPM

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More than 7000 rare diseases affect over 400 million people, posing significant challenges for medical research and healthcare. The integration of precision medicine with artificial intelligence offers promising solutions. This work introduces a classifier developed to discern whether research and news articles pertain to rare or non-rare diseases. Our methodology involves extracting 709 rare disease MeSH terms from Mondo and MeSH to improve rare disease categorization. We evaluate our classifier on abstracts from PubMed/MEDLINE and an expert-annotated news dataset, which includes news articles on four selected rare neurodevelopmental disorders (NDDs)—considered the largest category of rare diseases—from a total of 16 analyzed. We achieved F1 scores of 85% for abstracts and 71% for news articles, demonstrating robustness across both datasets and highlighting the potential of integrating artificial intelligence and ontologies to improve disease classification. Although the results are promising, they also indicate the need for further refinement in managing data heterogeneity. Our classifier improves the identification and categorization of medical information, essential for advancing research, enhancing information access, influencing policy, and supporting personalized treatments. Future work will focus on expanding disease classification to distinguish between attributes such as infectious and hereditary diseases, addressing data heterogeneity, and incorporating multilingual capabilities.

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Section: Related Workmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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Automatic Classification and Visualization of Text Data on Rare Diseases

Rei,

Pita Costa,

Zdolšek Draksler

2024

JPM

Self Cite

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“…This often results in small, imbalanced datasets [16]. We believe the approach we propose might also be used in other problems and domains that so far have relied on small expert annotated datasets or zero-shot transfer, such as healthcare [17].…”

Section: Motivationmentioning

confidence: 99%

Detecting Fine-Grained Emotions in Literature

Rei

Mladenić

2023

Applied Sciences

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Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model’s performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.

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“…To allow for the exploration of any health-related texts (such as scientific reports or news) we developed an automated classifier [8] that assigns to the input text the MeSH classes it relates to. The annotated text is then stored in Elasticsearch [27], from where it can be accessed through Lucene language queries, visualized over easy-tobuild dashboards, and connected through an API to the earlier described explorer (see figure 4 and read [12], [30] and [26] for more detail).…”

Section: Biomedical Research Explorermentioning

confidence: 99%

A global COVID-19 observatory, monitoring the pandemics through text mining and visualization

Massri

Costa

Bauer³

et al. 2022

IJCAI

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The global health situation due to the SARS-COV-2 pandemic motivated an unprecedented contribution of science and technology from companies and communities all over the world to fight COVID-19. In this paper, we present the impactful role of text mining and data analytics, exposed publicly through IRCAI's Coronavirus Watch portal. We will discuss the available technology and methodology, as well as the ongoing research based on the collected data.Povzetek: Opisana je vloga rudarjenja besedil in podatkovne analitike na primeru portala IRCAI's Coronavirus Watch.

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NewsMeSH: A new classifier designed to annotate health news with MeSH headings

Cited by 7 publications

References 22 publications

Automatic Classification and Visualization of Text Data on Rare Diseases

Automatic Classification and Visualization of Text Data on Rare Diseases

Detecting Fine-Grained Emotions in Literature

A global COVID-19 observatory, monitoring the pandemics through text mining and visualization

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