Proof pending. Core topic summary fields are still materializing.
Recent advancements in clinical natural language processing (NLP) are focusing on enhancing the extraction and understanding of complex medical information from unstructured texts. A notable trend is the development of hybrid neural-symbolic systems that outperform traditional generative models in extracting clinical follow-up instructions, achieving near-perfect accuracy in identifying action-date pairs. Additionally, researchers are exploring parameter-efficient multitask frameworks that allow for the adaptation of shared prompts across diverse clinical tasks, significantly reducing computational overhead while maintaining high performance. The field is also addressing specific challenges, such as detecting substance use in clinical texts, with ensemble approaches yielding impressive precision in low-resource settings. The introduction of large, annotated datasets, like the GS-BrainText, is further propelling the field by providing robust resources for training and validating NLP systems, ultimately aiming to improve clinical decision-making and patient care through more reliable and efficient text analysis.
Objective. Outpatient notes carry follow-up instructions pairing actions with future times ("MRI brain in two weeks"). Extracting (action, date) pairs supports scheduling and audit, but generative ext...
Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple clinical natural lan...
Extracting drug use information from unstructured Electronic Health Records remains a major challenge in clinical Natural Language Processing. While Large Language Models demonstrate advancements, the...
The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHabits Shared Task. Our team participated in subtask ...
We present GS-BrainText, a curated dataset of 8,511 brain radiology reports from the Generation Scotland cohort, of which 2,431 are annotated for 24 brain disease phenotypes. This multi-site dataset s...
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Agent Handoff
Canonical ID clinical-nlp | Route /topic/clinical-nlp
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}Use This Via API or MCP
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