Modelo preditivo de infecção hospitalar utilizando aprendizado de máquina
Machine learning is increasingly gaining ground in the health area due to its ability to improve disease prediction and assist professionals in conducting clinical treatments. Hospital infection is the most common negative event for hospitalized patients and continues to pose a serious threat to patient safety. The objective of this work was to find an optimized and efficient machine learning technique that can effectively predict the condition of nosocomial infection, identifying the main factors responsible for this condition. In this work, we used six machine learning techniques, the algorithms used in the work were Random Forest, Logistic Regression, KNN, Adaboost, Bagging and XGBoost; modern explainability techniques were also used for these algorithms. In this process, the data were divided into training and test data, the models were trained in a first moment with standard hyperparameters, in a second moment the models were trained with improved hyperparameters. The models that presented the best metrics were XGBoost and Random Forest, XGBoost presented the best result in all metrics, except for Precision, Random Forest obtained the second best result in accuracy and precision, in cross-validation the result was the same as XGBoost. For the explanation of the model, the SHAP library was used, it was evaluated how the value of each variable influenced the result achieved by the predictive model XGBoost, SHAP pointed out as the most important variables: NR_DIA_INTERNADO (number of days of hospitalization), CD_DOENCA_PRINCIPAL_E (ICD-10 International classification of diseases), DS_PROC_PRINCIPAL_E (Main procedure during hospitalization) and QT_DIAS_SONDA_VESICAL (Days that the patient had a urinary catheter). The study proved to be feasible for the adoption of machine learning in health research routines, in the work of the hospital infection committee and in innovation initiatives in health institutions in Brazil.
Citação
@online{patricia_pedrosa_moreira2023,
author = {Patricia Pedrosa Moreira , Mendes},
title = {Modelo preditivo de infecção hospitalar utilizando
aprendizado de máquina},
date = {2023-03-29},
doi = {10.11606/D.85.2023.tde-12072023-091827},
langid = {pt-BR},
abstract = {Machine learning is increasingly gaining ground in the
health area due to its ability to improve disease prediction and
assist professionals in conducting clinical treatments. Hospital
infection is the most common negative event for hospitalized
patients and continues to pose a serious threat to patient safety.
The objective of this work was to find an optimized and efficient
machine learning technique that can effectively predict the
condition of nosocomial infection, identifying the main factors
responsible for this condition. In this work, we used six machine
learning techniques, the algorithms used in the work were Random
Forest, Logistic Regression, KNN, Adaboost, Bagging and XGBoost;
modern explainability techniques were also used for these
algorithms. In this process, the data were divided into training and
test data, the models were trained in a first moment with standard
hyperparameters, in a second moment the models were trained with
improved hyperparameters. The models that presented the best metrics
were XGBoost and Random Forest, XGBoost presented the best result in
all metrics, except for Precision, Random Forest obtained the second
best result in accuracy and precision, in cross-validation the
result was the same as XGBoost. For the explanation of the model,
the SHAP library was used, it was evaluated how the value of each
variable influenced the result achieved by the predictive model
XGBoost, SHAP pointed out as the most important variables:
NR\_DIA\_INTERNADO (number of days of hospitalization),
CD\_DOENCA\_PRINCIPAL\_E (ICD-10 International classification of
diseases), DS\_PROC\_PRINCIPAL\_E (Main procedure during
hospitalization) and QT\_DIAS\_SONDA\_VESICAL (Days that the patient
had a urinary catheter). The study proved to be feasible for the
adoption of machine learning in health research routines, in the
work of the hospital infection committee and in innovation
initiatives in health institutions in Brazil.}
}