Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Performance of a large language model for identifying central line-associated bloodstream infections (CLABSI) using real clinical notes
12
Zitationen
5
Autoren
2024
Jahr
Abstract
We evaluated one of the first secure large language models approved for protected health information, for identifying central line-associated bloodstream infections (CLABSIs) using real clinical notes. Despite no pretraining, the model demonstrated rapid assessment and high sensitivity for CLABSI identification. Performance would improve with access to more patient data.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.764 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.674 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.234 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.