The Diagnostic Model of Neonatal Respiratory Distress Syndrome Based on Intelligent Algorithm
| Autor: | Bin Jing, Song-Chun Yang, Hai-Bin Meng, Dong-Sheng Zhao, Xue-Yi Shang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Decision support system
Neonatal respiratory distress syndrome Artificial neural network Computer science business.industry 02 engineering and technology medicine.disease Machine learning computer.software_genre Random forest Support vector machine 03 medical and health sciences Identification (information) 0302 clinical medicine Feature (computer vision) 030225 pediatrics 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence Risk assessment business computer |
| Zdroj: | 2016 8th International Conference on Information Technology in Medicine and Education (ITME). |
| DOI: | 10.1109/itme.2016.0078 |
| Popis: | The paper described a rapid decision support model for neonatal respiratory distress syndrome (NRDS), which was suitable for extensive neonatal related diseases for diagnose and identification rapidly. The available data, collected in No.307 hospital of PLA, was provided to several intelligent algorithms(artificial neural networks, random forests, support vector machines) to create a model for predicting the NRDS probability for newborns. It showed that prediction accuracy of the model for NRDS could reach up to 98.07% in test. We observed that predictions of the model are in agreement with the literature, demonstrating that model might be an important tool for supporting decision making in medical practice. Other feature of this method were the input parameters could be obtained easily in clinic and the implementation of the risk assessment could provide rapid decision support information for clinic. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|