We have 3 of 10 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
MedError, a machine-assisted, human-in-the-loop framework, improves the standardization, validity, and efficiency of clinical NLP error analysis, supporting more transparent and trustworthy model deployment.
Findings highlight key trade-offs among symbolic, generative, and instruction-tuned approaches for CGA and 4Ms phenotyping, suggesting that hybrid heuristic-LLM methods offer interpretability and stability, whereas large language models provide greater adaptability for complex clinical narratives.
This study investigates whether large language models (LLMs) can assist in identifying extraction errors and generating candidate rules to improve symbolic clinical NLP systems and suggests that LLMs can support scalable rule refinement for symbolic clinical NLP systems.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
