Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
PulseAugur coverage of Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing — every cluster mentioning Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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AI models detect clinical trial dosing errors with high accuracy · 2 sources tracked
Researchers have developed a method to detect dosing errors in clinical trials using domain-specific transformer embeddings and classification models. The study evaluated several language models, including ClinicalBERT,…
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New BERT Model Enhances Medical Device Recall Triage
Researchers have developed RecallRisk-BERT, a novel multi-task framework designed to improve the triage and assessment of medical device recalls. This model integrates textual data from recall narratives with structured…
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New method improves causal discovery in Large Behavioural Models
Researchers have developed a method to improve the accuracy of causal discovery in Large Behavioural Models (LBMs) by addressing issues with embedding proximity. Standard biomedical language models incorrectly associate…
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New PubMedCausal Corpus Enhances Biomedical Causal Relation Extraction
Researchers have introduced PubMedCausal, a new corpus designed for causal relation extraction in biomedical text. This dataset, derived from PubMed abstracts, offers span-level annotations for 3,945 causal rows and 6,4…
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New Dataset Extracts Drug Insights from Reddit
Researchers have developed ReDose, a dataset of 6,435 Reddit posts focused on substance use, to help physicians better understand real-world drug usage beyond clinical overdose cases. The dataset, annotated by a toxicol…