Hospital-acquired infections surveillance: The machine-learning algorithm mirrors National Healthcare Safety Network definitions

Surveillance of hospital-acquired infections (HAIs) is the foundation of infection control. Machine learning (ML) has been demonstrated to be a valuable tool for HAI surveillance. We compared manual surveillance with a supervised, semiautomated, ML method, and we explored the types of infection and...

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Published in:Infection control and hospital epidemiology Vol. 45; no. 5; pp. 604 - 608
Main Authors: Lukasewicz Ferreira, Stephani Amanda, Franco Meneses, Arateus Crysham, Vaz, Tiago Andres, da Fontoura Carvalho, Otavio Luiz, Hubner Dalmora, Camila, Pressotto Vanni, Daiane, Ribeiro Berti, Isabele, Pires dos Santos, Rodrigo
Format: Journal Article
Language:English
Published: New York, USA Cambridge University Press 01.05.2024
Subjects:
Accuracy
Algorithms
Antibiotics
Artificial intelligence
Automation
Classification
Comorbidity
Coronaviruses
COVID-19
Cross Infection - epidemiology
Disease control
Electronic health records
Hospitals
Humans
Infection Control - methods
Machine learning
Methods
Nosocomial infections
Original Article
Patient safety
Pneumonia
Surgical site infections
Urinary tract diseases
Urinary tract infections
Urogenital system
Variables
Ventilators
Brazil
ISSN:0899-823X, 1559-6834, 1559-6834
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Summary:Surveillance of hospital-acquired infections (HAIs) is the foundation of infection control. Machine learning (ML) has been demonstrated to be a valuable tool for HAI surveillance. We compared manual surveillance with a supervised, semiautomated, ML method, and we explored the types of infection and features of importance depicted by the model. From July 2021 to December 2021, a semiautomated surveillance method based on the ML random forest algorithm, was implemented in a Brazilian hospital. Inpatient records were independently manually searched by the local team, and a panel of independent experts reviewed the ML semiautomated results for confirmation of HAI. Among 6,296 patients, manual surveillance classified 183 HAI cases (2.9%), and a semiautomated method found 299 HAI cases (4.7%). The semiautomated method added 77 respiratory infections, which comprised 93.9% of the additional HAIs. The ML model considered 447 features for HAI classification. Among them, 148 features (33.1%) were related to infection signs and symptoms; 101 (22.6%) were related to patient severity status, 51 features (11.4%) were related to bacterial laboratory results; 40 features (8.9%) were related to invasive procedures; 34 (7.6%) were related to antibiotic use; and 31 features (6.9%) were related to patient comorbidities. Among these 447 features, 229 (51.2%) were similar to those proposed by NHSN as criteria for HAI classification. The ML algorithm, which included most NHSN criteria and >200 features, augmented the human capacity for HAI classification. Well-documented algorithm performances may facilitate the incorporation of AI tools in clinical or epidemiological practice and overcome the drawbacks of traditional HAI surveillance.
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ISSN:0899-823X
1559-6834
1559-6834
DOI:10.1017/ice.2023.224