Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages
8
Zitationen
3
Autoren
2025
Jahr
Abstract
Fine-tuned LLMs can categorize unstructured EHR data with high accuracy, closely approximating the performance of domain experts. This approach significantly reduces the time and costs associated with manual data labeling, demonstrating the potential to streamline the processing of large-scale healthcare data for AI applications.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.396 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.729 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.270 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.702 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.437 Zit.