Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units
Nowadays, sepsis is considered a global burden disease with anannual incidence of three million neonatal cases. Nevertheless, there are no homogeneous criteria for neonatal sepsis. Furthermore, adult sepsis scores don’t work properly in neonatal Intensive Care Units (ICUs) settings due to the specif...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/151667 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/373/311 |
| Aporte de: |
| id |
I19-R120-10915-151667 |
|---|---|
| record_format |
dspace |
| spelling |
I19-R120-10915-1516672023-04-18T20:03:58Z http://sedici.unlp.edu.ar/handle/10915/151667 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/373/311 issn:2451-7496 Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units Muñoz Lezcano, Sergio López, Fernando Corbi, Alberto 2022-10 2022 2023-04-18T17:00:42Z en Ciencias Informáticas Sepsis CAD NICU MIMIC-III Artificial Intelligence Nowadays, sepsis is considered a global burden disease with anannual incidence of three million neonatal cases. Nevertheless, there are no homogeneous criteria for neonatal sepsis. Furthermore, adult sepsis scores don’t work properly in neonatal Intensive Care Units (ICUs) settings due to the specific characteristics of the neonates' immune systems. This work describes and surveys a machine-learning computer-aided diagnosis approach for predicting sepsis mortality in neonatal ICUs. The survey is based on a retrospective cohort study in which each patient has an initial sepsis-related diagnosis in the first 24h after ICU admission. Our experiments are based on four different machine-learning techniques: decision trees, random forests, support vector machines and artificial neural networks. The predictive power was assessed using accuracy, sensitivity, and specificity. The importance of the variables was obtained automatically through data science techniques using R. The approach with the best performance was the random forest, which achieves an accuracy of 97% in the prediction of mortality. Sociedad Argentina de Informática e Investigación Operativa Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 64-73 |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Sepsis CAD NICU MIMIC-III Artificial Intelligence |
| spellingShingle |
Ciencias Informáticas Sepsis CAD NICU MIMIC-III Artificial Intelligence Muñoz Lezcano, Sergio López, Fernando Corbi, Alberto Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| topic_facet |
Ciencias Informáticas Sepsis CAD NICU MIMIC-III Artificial Intelligence |
| description |
Nowadays, sepsis is considered a global burden disease with anannual incidence of three million neonatal cases. Nevertheless, there are no homogeneous criteria for neonatal sepsis. Furthermore, adult sepsis scores don’t work properly in neonatal Intensive Care Units (ICUs) settings due to the specific characteristics of the neonates' immune systems. This work describes and surveys a machine-learning computer-aided diagnosis approach for predicting sepsis mortality in neonatal ICUs. The survey is based on a retrospective cohort study in which each patient has an initial sepsis-related diagnosis in the first 24h after ICU admission. Our experiments are based on four different machine-learning techniques: decision trees, random forests, support vector machines and artificial neural networks. The predictive power was assessed using accuracy, sensitivity, and specificity. The importance of the variables was obtained automatically through data science techniques using R. The approach with the best performance was the random forest, which achieves an accuracy of 97% in the prediction of mortality. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Muñoz Lezcano, Sergio López, Fernando Corbi, Alberto |
| author_facet |
Muñoz Lezcano, Sergio López, Fernando Corbi, Alberto |
| author_sort |
Muñoz Lezcano, Sergio |
| title |
Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| title_short |
Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| title_full |
Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| title_fullStr |
Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| title_full_unstemmed |
Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| title_sort |
computer aided prediction of sepsis-related mortality risk in neonatal intensive care units |
| publishDate |
2022 |
| url |
http://sedici.unlp.edu.ar/handle/10915/151667 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/373/311 |
| work_keys_str_mv |
AT munozlezcanosergio computeraidedpredictionofsepsisrelatedmortalityriskinneonatalintensivecareunits AT lopezfernando computeraidedpredictionofsepsisrelatedmortalityriskinneonatalintensivecareunits AT corbialberto computeraidedpredictionofsepsisrelatedmortalityriskinneonatalintensivecareunits |
| _version_ |
1765659998324523008 |