An AI system based on NLP and machine learning has been developed to identify surgical site infections (SSIs) from hospital discharge letters. After advanced pre-processing and imbalance handling, BERT-FT achieved the best performance (F1=0.79), outperforming TF-IDF and W2V. Large language models (LLMs) showed limitations. The system could support semi-automatic SSI surveillance, with prospects for optimisation in translations, prompts, and infrastructure.
Sorveglianza delle infezioni del sito chirurgico tramite applicazione di natural language processing su lettere di dimissione ospedaliera: studio retrospettivo presso un ospedale universitario
Zotti, NunzioPrimo
;Arzilli, GuglielmoSecondo
;Baglivo, Francesco;De Angelis, Luigi;Porretta, AndreaPenultimo
;Rizzo, CaterinaUltimo
2025-01-01
Abstract
An AI system based on NLP and machine learning has been developed to identify surgical site infections (SSIs) from hospital discharge letters. After advanced pre-processing and imbalance handling, BERT-FT achieved the best performance (F1=0.79), outperforming TF-IDF and W2V. Large language models (LLMs) showed limitations. The system could support semi-automatic SSI surveillance, with prospects for optimisation in translations, prompts, and infrastructure.File in questo prodotto:
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