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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| Vydáno v: | Infection control and hospital epidemiology Ročník 45; číslo 5; s. 604 - 608 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, USA
Cambridge University Press
01.05.2024
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| ISSN: | 0899-823X, 1559-6834, 1559-6834 |
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| Abstract | 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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| AbstractList | 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.BACKGROUNDSurveillance 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.METHODSFrom 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.RESULTSAmong 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.CONCLUSIONThe 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. Background: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.Methods: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.Results: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.Conclusion: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. 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. |
| Author | da Fontoura Carvalho, Otavio Luiz Lukasewicz Ferreira, Stephani Amanda Vaz, Tiago Andres Hubner Dalmora, Camila Pressotto Vanni, Daiane Ribeiro Berti, Isabele Pires dos Santos, Rodrigo Franco Meneses, Arateus Crysham |
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| Cites_doi | 10.1016/j.infpip.2021.100167 10.1086/664048 10.1177/1460458216656471 10.1016/j.jhin.2017.09.002 10.1017/ice.2021.362 10.1016/j.ajic.2017.11.006 10.1016/0195-6701(93)90028-X 10.1056/NEJMoa1801550 10.1161/CIRCULATIONAHA.115.001593 10.1016/j.artmed.2005.03.002 10.1017/ash.2022.312 10.1016/j.ajic.2020.02.010 10.1016/j.jiph.2020.06.006 |
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| References | 1993; 23 2021; 3 2022; 3 2018; 379 2015; 132 2006; 37 2016; 21 2014; 15 2020; 48 2020; 13 2022; 43 2008; 136 2023; 3 2018; 99 2004; 107 2012; 33 2018; 46 2018; 24 S0899823X23002246_ref2 S0899823X23002246_ref1 S0899823X23002246_ref13 Fernández-Delgado (S0899823X23002246_ref12) 2014; 15 S0899823X23002246_ref4 S0899823X23002246_ref10 S0899823X23002246_ref11 S0899823X23002246_ref3 S0899823X23002246_ref6 S0899823X23002246_ref16 S0899823X23002246_ref17 S0899823X23002246_ref14 S0899823X23002246_ref8 S0899823X23002246_ref7 S0899823X23002246_ref15 S0899823X23002246_ref20 Cohen (S0899823X23002246_ref18) 2004; 107 Cohen (S0899823X23002246_ref19) 2008; 136 Mitchell (S0899823X23002246_ref5) 2016; 21 Ferreira (S0899823X23002246_ref9) 2022; 3 |
| References_xml | – volume: 46 start-page: 487 year: 2018 end-page: 491 article-title: A systematic approach to quantifying infection prevention staffing and coverage needs publication-title: J Infect Control – volume: 3 start-page: e25 year: 2023 article-title: Automating surveillance for healthcare-associated infections: rationale and current realities (part I/III) publication-title: Antimicrob Steward Healthc Epidemiol – volume: 107 start-page: 716 year: 2004 end-page: 720 article-title: An application of one-class support vector machine to nosocomial infection detection publication-title: Stud Health Technol Inform – volume: 33 start-page: 283 year: 2012 end-page: 291 article-title: Prevalence of healthcare-associated infections in acute-care hospitals in Jacksonville, Florida publication-title: Infect Control Hosp Epidemiol – volume: 132 start-page: 1920 year: 2015 end-page: 1930 article-title: Machine learning in medicine publication-title: Circulation – volume: 15 start-page: 3133 year: 2014 end-page: 3181 article-title: Do we need hundreds of classifiers to solve real-world classification problems? publication-title: J Mach Learn Res – volume: 379 start-page: 1732 year: 2018 end-page: 1744 article-title: Changes in prevalence of healthcare-associated infections in US hospitals publication-title: N Engl J Med – volume: 136 start-page: 21 year: 2008 end-page: 26 article-title: Novelty detection using one-class Parzen density estimator. An application to surveillance of nosocomial infections publication-title: Stud Health Technol Inform – volume: 48 start-page: 609 year: 2020 end-page: 614 article-title: How much is adequate staffing for infection control? A deterministic approach through the lens of workload indicators of staffing need publication-title: Am J Infect Control – volume: 13 start-page: 1061 year: 2020 end-page: 1077 article-title: Artificial intelligence-based tools to control healthcare associated infections: a systematic review of the literature publication-title: J Infect Public Health – volume: 3 start-page: 1 issue: 8 year: 2022 end-page: 17 article-title: An effective infection surveillance assistant robot impacts care delivery by reducing burden on infection control professional staff publication-title: N Engl J Catalyst – volume: 99 start-page: 1 year: 2018 end-page: 7 article-title: Impact of electronic healthcare-associated infection surveillance software on infection prevention resources: a systematic review of the literature publication-title: J Hosp Infect – volume: 23 start-page: 229 year: 1993 end-page: 242 article-title: An evaluation of surveillance methods for detecting infections in hospital inpatients publication-title: J Hosp Infect – volume: 43 start-page: 12 year: 2022 end-page: 25 article-title: The impact of coronavirus disease 2019 (COVID-19) on healthcare-associated infections in 2020: a summary of data reported to the National Healthcare Safety Network publication-title: Infect Control Hosp Epidemiol – volume: 21 start-page: 36e40 year: 2016 article-title: Time spent by infection control professionals undertaking healthcare-associated infection surveillance: a multicentered cross-sectional study publication-title: Infect Dis Health – volume: 3 start-page: 100167 year: 2021 article-title: Automated healthcare-associated infection surveillance using an artificial intelligence algorithm publication-title: Infect Prev Pract – volume: 24 start-page: 24 year: 2018 end-page: 42 article-title: Detecting hospital-acquired infections: a document classification approach using support vector machines and gradient tree boosting publication-title: Health Informatics J – volume: 37 start-page: 7 year: 2006 end-page: 18 article-title: Learning from imbalanced data in surveillance of nosocomial infection publication-title: Artif Intell Med – volume: 107 start-page: 716 year: 2004 ident: S0899823X23002246_ref18 article-title: An application of one-class support vector machine to nosocomial infection detection publication-title: Stud Health Technol Inform – ident: S0899823X23002246_ref10 doi: 10.1016/j.infpip.2021.100167 – ident: S0899823X23002246_ref13 doi: 10.1086/664048 – ident: S0899823X23002246_ref17 doi: 10.1177/1460458216656471 – ident: S0899823X23002246_ref7 doi: 10.1016/j.jhin.2017.09.002 – volume: 136 start-page: 21 year: 2008 ident: S0899823X23002246_ref19 article-title: Novelty detection using one-class Parzen density estimator. An application to surveillance of nosocomial infections publication-title: Stud Health Technol Inform – ident: S0899823X23002246_ref2 doi: 10.1017/ice.2021.362 – ident: S0899823X23002246_ref16 doi: 10.1016/j.ajic.2017.11.006 – volume: 15 start-page: 3133 year: 2014 ident: S0899823X23002246_ref12 article-title: Do we need hundreds of classifiers to solve real-world classification problems? publication-title: J Mach Learn Res – ident: S0899823X23002246_ref14 doi: 10.1016/0195-6701(93)90028-X – ident: S0899823X23002246_ref1 doi: 10.1056/NEJMoa1801550 – ident: S0899823X23002246_ref4 – ident: S0899823X23002246_ref11 doi: 10.1161/CIRCULATIONAHA.115.001593 – ident: S0899823X23002246_ref3 – ident: S0899823X23002246_ref20 doi: 10.1016/j.artmed.2005.03.002 – ident: S0899823X23002246_ref6 doi: 10.1017/ash.2022.312 – volume: 21 start-page: 36e40 year: 2016 ident: S0899823X23002246_ref5 article-title: Time spent by infection control professionals undertaking healthcare-associated infection surveillance: a multicentered cross-sectional study publication-title: Infect Dis Health – volume: 3 start-page: 1 year: 2022 ident: S0899823X23002246_ref9 article-title: An effective infection surveillance assistant robot impacts care delivery by reducing burden on infection control professional staff publication-title: N Engl J Catalyst – ident: S0899823X23002246_ref15 doi: 10.1016/j.ajic.2020.02.010 – ident: S0899823X23002246_ref8 doi: 10.1016/j.jiph.2020.06.006 |
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| Title | Hospital-acquired infections surveillance: The machine-learning algorithm mirrors National Healthcare Safety Network definitions |
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