Browsing by Author "Lobel H."

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    A comparative dataset: Bridging COVID-19 and other diseases through epistemonikos and CORD-19 evidence
    (Springer, 2023) Carvallo A.; Parra D.; Lobel H.; Rada G.; CEDEUS (Chile)
    © 2023 The Author(s)The COVID-19 pandemic has underlined the need for reliable information for clinical decision-making and public health policies. As such, evidence-based medicine (EBM) is essential in identifying and evaluating scientific documents pertinent to novel diseases, and the accurate classification of biomedical text is integral to this process. Given this context, we introduce a comprehensive, curated dataset composed of COVID-19-related documents. This dataset includes 20,047 labeled documents that were meticulously classified into five distinct categories: systematic reviews (SR), primary study randomized controlled trials (PS-RCT), primary study non-randomized controlled trials (PS-NRCT), broad synthesis (BS), and excluded (EXC). The documents, labeled by collaborators from the Epistemonikos Foundation, incorporate information such as document type, title, abstract, and metadata, including PubMed id, authors, journal, and publication date. Uniquely, this dataset has been curated by the Epistemonikos Foundation and is not readily accessible through conventional web-scraping methods, thereby attesting to its distinctive value in this field of research. In addition to this, the dataset also includes a vast evidence repository comprising 427,870 non-COVID-19 documents, also categorized into SR, PS-RCT, PS-NRCT, BS, and EXC. This additional collection can serve as a valuable benchmark for subsequent research. The comprehensive nature of this open-access dataset and its accompanying resources is poised to significantly advance evidence-based medicine and facilitate further research in the domain.
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    News Gathering: Leveraging Transformers to Rank News
    (Springer Science and Business Media Deutschland GmbH, 2024) Munoz C.; Apolo M.J.; Ojeda M.; Lobel H.; Mendoza M.
    News media outlets disseminate information across various platforms. Often, these posts present complementary content and perspectives on the same news story. However, to compile a set of related news articles, users must thoroughly scour multiple sources and platforms, manually identifying which publications pertain to the same story. This tedious process hinders the speed at which journalists can perform essential tasks, notably fact-checking. To tackle this problem, we created a dataset containing both related and unrelated news pairs. This dataset allows us to develop information retrieval models grounded in the principle of binary relevance. Recognizing that many Transformer-based models might be suited for this task but could overemphasize relationships based on lexical connections, we tailored a dataset to fine-tune these models to focus on semantically relevant connections in the news domain. To craft this dataset, we introduced a methodology to identify pairs of news stories that are lexically similar yet refer to different events and pairs that discuss the same event but have distinct lexical structures. This design compels Transformers to recognize semantic connections between stories, even when their lexical similarities might be absent. Following a human-annotation assessment, we reveal that BERT outperformed other techniques, excelling even in challenging test cases. To ensure the reproducibility of our approach, we have made the dataset and top-performing models publicly available.