A Neural Network Approach to Predicting Mortality in Pediatric Intensive Care
(2018) MASM01 20181Mathematical Statistics
- Abstract
- In this Master’s thesis, data from a pediatric intensive care unit for children and
youth at the Skåne University Hospital in Lund, Sweden, are used for predicting
child mortality and investigating if the PIM2 model can be improved using neural
networks and blood gas test results. In the analysis, 1155 patient admissions were
included after removing patients without tests witin the first hour of admission.
Three different multilayer perceptron neural networks were created. To choose the
variables and the parameters for the networks, a grid search and uni- and multi-
variate logistic regressions were used; 5-fold cross validation was applied to ensure
the networks’ adequate performance on new data. The results show that neural
... (More) - In this Master’s thesis, data from a pediatric intensive care unit for children and
youth at the Skåne University Hospital in Lund, Sweden, are used for predicting
child mortality and investigating if the PIM2 model can be improved using neural
networks and blood gas test results. In the analysis, 1155 patient admissions were
included after removing patients without tests witin the first hour of admission.
Three different multilayer perceptron neural networks were created. To choose the
variables and the parameters for the networks, a grid search and uni- and multi-
variate logistic regressions were used; 5-fold cross validation was applied to ensure
the networks’ adequate performance on new data. The results show that neural
networks can predict child mortality in the ICU and are a feasible alternative to
PIM2. Using blood gas variables improves the predictions, especially in addition
to other physiological variables. Further research is needed to conclude if the im-
provement outweighs the extra computational effort, and should focus on a larger
dataset with a wider grid search to find the optimal parameters for the networks (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8951611
- author
- Horvat, Kaja
- supervisor
- organization
- course
- MASM01 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 8951611
- date added to LUP
- 2018-06-20 10:53:57
- date last changed
- 2018-06-21 10:32:46
@misc{8951611, abstract = {{In this Master’s thesis, data from a pediatric intensive care unit for children and youth at the Skåne University Hospital in Lund, Sweden, are used for predicting child mortality and investigating if the PIM2 model can be improved using neural networks and blood gas test results. In the analysis, 1155 patient admissions were included after removing patients without tests witin the first hour of admission. Three different multilayer perceptron neural networks were created. To choose the variables and the parameters for the networks, a grid search and uni- and multi- variate logistic regressions were used; 5-fold cross validation was applied to ensure the networks’ adequate performance on new data. The results show that neural networks can predict child mortality in the ICU and are a feasible alternative to PIM2. Using blood gas variables improves the predictions, especially in addition to other physiological variables. Further research is needed to conclude if the im- provement outweighs the extra computational effort, and should focus on a larger dataset with a wider grid search to find the optimal parameters for the networks}}, author = {{Horvat, Kaja}}, language = {{eng}}, note = {{Student Paper}}, title = {{A Neural Network Approach to Predicting Mortality in Pediatric Intensive Care}}, year = {{2018}}, }