A privacy-preserving local hybrid framework combining a compact open-weight SLM with regular expression rules achieves high specificity and precision for extracting six uncoded clinical features from multilingual primary care narratives.
This framework enables the generation of deidentified Japanese EHR narratives that preserve contextual structure for auditing while supporting structured entity extraction, thereby addressing the trade-off between privacy protection and reproducibility in real-world data research.
Nobuo Mochizuki, Atsushi Ikeda, Hideyuki Ohshima et al.· JMIR Medical Informatics· 0 citations
SNGR demonstrates how unstructured EHR data can be transformed into analysis-ready datasets for RWE generation, and provides a scalable and methodologically robust infrastructure for next-generation registries, particularly suited for oncology and other data-rich clinical domains.
J. Marin-Corral, N. Iglesias, S. Menke et al.· ESMO real world data and dig...· 0 citations
This study provides a systematic comparison of several open-source LLMs on a structured intestinal symptom extraction task and concludes that Qwen3 models offer a favorable balance between accuracy and efficiency, making them suitable for resource-constrained scenarios.
Xin-Yue Zhang, Quan-Yu Wang, Beibei Liu et al.· Journal of Medical Internet...· 0 citations
Health care organizations increasingly require structured, patient-level clinical variables for treatment decisions, operational workflows, quality measurement, and clinical trial screening. Relevant information is often fragmented across heterogeneous electronic health record (EHR) systems, unstructured formats, a...
Timothy J. Stuhlmiller, AJ Rabe, Jeff Rapp et al.· AI in Precision Oncology· 0 citations
Infectious diseases remain a major contributor to morbidity in hospital settings, and clinically relevant information is often embedded within unstructured Electronic Health Record (EHR) narratives. This study presents a structured prompt-based Large Language Model (LLM) Natural Language Processing (NLP) pipeline for p...
G. Alfattni· Engineering, Technology &...· 0 citations
BACKGROUND
Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automa...
Yi Xin, K. W. Davis, Wu-Chen Su et al.· JAMIA Journal of the America...· 0 citations
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