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New MiNER model enhances malaria entity recognition in clinical texts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MiNER, a fine-tuned biomedical language model designed for extracting information about malaria from clinical texts. The model utilizes BioBERT, a pre-trained language model, and is trained on a curated corpus of malaria-related scientific articles. Experiments show that MiNER significantly outperforms other encoding and machine learning algorithms in precision, recall, and accuracy for named entity recognition. The team has also released their annotated dataset to facilitate further research in malaria information extraction. AI

IMPACT This model could improve the efficiency of biomedical research by automating information extraction from malaria literature.

RANK_REASON The item is an academic paper detailing a new fine-tuned language model for a specific biomedical task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MiNER model enhances malaria entity recognition in clinical texts

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The item is an academic paper detailing a new fine-tuned language model for a specific biomedical task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · V. S. Anoop, Devika N ·

    MiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts

    arXiv:2609.00073v1 Announce Type: new Abstract: Malaria remains a significant global health burden, necessitating continuous research efforts to understand its complex molecular mechanisms, epidemiology, and potential therapeutic interventions. Extracting essential biomedical inf…

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