Feature Selection Based on a Genetic Algorithm for Optimizing Weaning Success

被引：0
|
作者：
Rosati, Samanta [1 ]
Scotto, Andrea [1 ]
Fanelli, Vito [2 ]
Balestra, Gabriella [1 ]
机构：
[1] Politecn Torino, Dept Elect & Telecommun, Turin, Italy
[2] Univ Torino, Dept Surg Sci, Turin, Italy
来源：
CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023 | 2023年 / 302卷
关键词：
MIMIC; mechanical ventilation; weaning; genetic algorithms; feature selection;
D O I：
10.3233/SHTI230206
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Finding the right time for weaning from ventilator is a difficult clinical decision. Several systems based on machine or deep learning are reported in literature. However, the results of these applications are not completely satisfactory and may be improved. An important aspect is represented by the features used as input of these systems. In this paper we present the results of the application of genetic algorithms to perform feature selection on a dataset containing 13688 patients under mechanical ventilation characterizing by 58 variables, extracted from the MIMIC III database. The results show that all features are important, but four of them are essential: 'Sedation_days', 'Mean_Airway_Pressure', 'PaO2', and 'Chloride'. This is only the initial step to obtain a tool to be added to the other clinical indices for minimize the risk of extubation failure.
引用
收藏
页码：566 / 570
页数：5
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