Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit
The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-06-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/21/6/603 |
| _version_ | 1811186039961681920 |
|---|---|
| author | Sergio Martínez-Agüero Inmaculada Mora-Jiménez Jon Lérida-García Joaquín Álvarez-Rodríguez Cristina Soguero-Ruiz |
| author_facet | Sergio Martínez-Agüero Inmaculada Mora-Jiménez Jon Lérida-García Joaquín Álvarez-Rodríguez Cristina Soguero-Ruiz |
| author_sort | Sergio Martínez-Agüero |
| collection | DOAJ |
| description | The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for the patient, for the system, and for society in general. Because of the critical health status of patients in the intensive care unit (ICU), time is critical to identify bacteria and their resistance to antibiotics. Since common antibiotics resistance tests require between 24 and 48 h after the culture is collected, we propose to apply machine learning (ML) techniques to determine whether a bacterium will be resistant to different families of antimicrobials. For this purpose, clinical and demographic features from the patient, as well as data from cultures and antibiograms are considered. From a population point of view, we also show graphically the relationship between different bacteria and families of antimicrobials by performing correspondence analysis. Results of the ML techniques evidence non-linear relationships helping to identify antimicrobial resistance at the ICU, with performance dependent on the family of antimicrobials. A change in the trend of antimicrobial resistance is also evidenced. |
| first_indexed | 2024-04-11T13:40:29Z |
| format | Article |
| id | doaj.art-36cb0e32d25b4f39acea44133c00b227 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:40:29Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-36cb0e32d25b4f39acea44133c00b2272022-12-22T04:21:16ZengMDPI AGEntropy1099-43002019-06-0121660310.3390/e21060603e21060603Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care UnitSergio Martínez-Agüero0Inmaculada Mora-Jiménez1Jon Lérida-García2Joaquín Álvarez-Rodríguez3Cristina Soguero-Ruiz4Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid 28943, SpainDepartment of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid 28943, SpainDepartment of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid 28943, SpainIntensive Care Department, University Hospital of Fuenlabrada, Madrid 28942, SpainDepartment of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid 28943, SpainThe presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for the patient, for the system, and for society in general. Because of the critical health status of patients in the intensive care unit (ICU), time is critical to identify bacteria and their resistance to antibiotics. Since common antibiotics resistance tests require between 24 and 48 h after the culture is collected, we propose to apply machine learning (ML) techniques to determine whether a bacterium will be resistant to different families of antimicrobials. For this purpose, clinical and demographic features from the patient, as well as data from cultures and antibiograms are considered. From a population point of view, we also show graphically the relationship between different bacteria and families of antimicrobials by performing correspondence analysis. Results of the ML techniques evidence non-linear relationships helping to identify antimicrobial resistance at the ICU, with performance dependent on the family of antimicrobials. A change in the trend of antimicrobial resistance is also evidenced.https://www.mdpi.com/1099-4300/21/6/603machine learningclinical dataantibiogramfeature selectioncorrespondence analysiscultureantimicrobial resistancebacteriaintensive care unit |
| spellingShingle | Sergio Martínez-Agüero Inmaculada Mora-Jiménez Jon Lérida-García Joaquín Álvarez-Rodríguez Cristina Soguero-Ruiz Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit Entropy machine learning clinical data antibiogram feature selection correspondence analysis culture antimicrobial resistance bacteria intensive care unit |
| title | Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit |
| title_full | Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit |
| title_fullStr | Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit |
| title_full_unstemmed | Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit |
| title_short | Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit |
| title_sort | machine learning techniques to identify antimicrobial resistance in the intensive care unit |
| topic | machine learning clinical data antibiogram feature selection correspondence analysis culture antimicrobial resistance bacteria intensive care unit |
| url | https://www.mdpi.com/1099-4300/21/6/603 |
| work_keys_str_mv | AT sergiomartinezaguero machinelearningtechniquestoidentifyantimicrobialresistanceintheintensivecareunit AT inmaculadamorajimenez machinelearningtechniquestoidentifyantimicrobialresistanceintheintensivecareunit AT jonleridagarcia machinelearningtechniquestoidentifyantimicrobialresistanceintheintensivecareunit AT joaquinalvarezrodriguez machinelearningtechniquestoidentifyantimicrobialresistanceintheintensivecareunit AT cristinasogueroruiz machinelearningtechniquestoidentifyantimicrobialresistanceintheintensivecareunit |