Surgical Site Infections (SSIs) represent a significant proportion of healthcare-associated infections and pose a major burden in hospital settings. Traditional SSI surveillance, based on manual review of free-text clinical records, is time-consuming and poorly scalable. This study aims to develop and evaluate a semi-automated surveillance system based on artificial intelligence (AI) for SSI detection using Italian-language clinical records from a large tertiary hospital.
An NLP-Based Approach for Surgical Site Infection Surveillance Using Hospital Discharge Letters
Renda, A;
2025-01-01
Abstract
Surgical Site Infections (SSIs) represent a significant proportion of healthcare-associated infections and pose a major burden in hospital settings. Traditional SSI surveillance, based on manual review of free-text clinical records, is time-consuming and poorly scalable. This study aims to develop and evaluate a semi-automated surveillance system based on artificial intelligence (AI) for SSI detection using Italian-language clinical records from a large tertiary hospital.File in questo prodotto:
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