Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Statistical Analysis
3.2. Predictive Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Parameters | Description |
---|---|
C, alpha | Regularization parameter |
Gamma | Kernel coefficient |
Kernel | Type of kernel |
n_estimators | The number of trees in the forest. |
learning_rate | Learning rate schedule for weight updates. |
objective | Objective function to optimize the model’s parameter |
rule | Rule for splitting information |
min_node_size | Minimum size of each node in the tree |
hidden_layer_size | Number of neurons in hidden layer |
max_iteration | Maximum number of iteration |
solver | Solver for weight optimization |
criterion | The function to measure the quality of a split. |
max_features | The number of features to consider when looking for the best split |
penalty | The norm of the penalty |
HABSIs N = 65 | Non-HABSIs N = 1138 | p-Value | |
---|---|---|---|
Sex, boys | 38 (5.71%) | 628 (94.29%) | 0.222 |
Gestational age, weeks (Median, IQR) | 30 (27–33) | 34 (32–37) | <0.000 |
Birthweight, gr (Median, IQR) | 1140 (820–1470) | 1940 (1442.50–2833.75) | <0.000 |
Length of total hospital stay, days (Median, IQR) | 54 (26–83) | 20 (12–33) | <0.000 |
Umbilical line catheterization, days (Median, IQR) | 5 (0–8) | 0 (0–6) | <0.000 |
Central line catheterization, days (Median, IQR) | 14 (7–38) | 0 (0–4) | <0.000 |
OR | 95% CI | p-Value | |
---|---|---|---|
Sex, boys | 1.031 | 0.263–3.891 | 0.510 |
Gestational age, weeks | 1.011 | 1.048–1.137 | 0.859 |
Birthweight, gr | 0.999 | 0.999–1.098 | 0.038 |
Length of total hospital stay, days | 1.023 | 0.994–1.098 | 0.327 |
Umbilical line catheterization, days | 1.072 | 0.994–1.098 | 0.934 |
Central line catheterization, days | 1.000 | 1.008–1.149 | 0.000 |
AI Model | Parameters |
---|---|
SVC | ‘C’: 1, ‘gamma’: 0.0001, ‘kernel’: ‘rbf’ |
CATBOOST | ‘n_estimators’: 100, ‘learning_rate’: 0.01 |
XGB | ‘learning_rate’: 0.01, ‘n_estimators ’: 100, ‘objective’: ‘binary’ |
RFC | ‘min_node_size’: 0, ‘rule’: ‘gini’, ‘n_estimators’: 100 |
MLP | ‘alpha’: 1e-05, ‘hidden_layer_sizes’: 14, ‘max_iter’: 1000, ‘random_state’: 1, |
‘solver’: ‘lbfgs’ | |
RF | ‘criterion’: ’entropy’, ‘max_depth’: 4, ‘max_features’: ‘auto’, ‘n_estimators’: 200 |
LR | ‘C’: 1.0, ‘penalty’: ‘l2’ |
AI Model | Train | Test | |||
---|---|---|---|---|---|
Accuracy | Accuracy | AUC | F1-Score | F1-Macro | |
SVC | 0.9501 | 0.9461 | 0.5357 | 0.95 | 0.5527 |
CATBOOST | 0.9438 | 0.9419 | 0.5670 | 0.94 | 0.5670 |
XGB | 0.9428 | 0.9378 | 0.5313 | 0.94 | 0.5427 |
RFC | 0.9469 | 0.9419 | 0.5335 | 0.94 | 0.5474 |
MLP | 0.9511 | 0.9461 | 0.6027 | 0.95 | 0.6439 |
RF | 0.9511 | 0.9419 | 0.5335 | 0.94 | 0.5475 |
LR | 0.9490 | 0.9461 | 0.6027 | 0.95 | 0.6439 |
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Montella, E.; Ferraro, A.; Sperlì, G.; Triassi, M.; Santini, S.; Improta, G. Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study. Int. J. Environ. Res. Public Health 2022, 19, 2498. https://doi.org/10.3390/ijerph19052498
Montella E, Ferraro A, Sperlì G, Triassi M, Santini S, Improta G. Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study. International Journal of Environmental Research and Public Health. 2022; 19(5):2498. https://doi.org/10.3390/ijerph19052498
Chicago/Turabian StyleMontella, Emma, Antonino Ferraro, Giancarlo Sperlì, Maria Triassi, Stefania Santini, and Giovanni Improta. 2022. "Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study" International Journal of Environmental Research and Public Health 19, no. 5: 2498. https://doi.org/10.3390/ijerph19052498