Deep learning approach on tabular data to predict early-onset neonatal sepsis
| Autor: | Md. Habibur Rahman, Adib Al Shaeed Khan, Redwan Hasif Alvi, Rashedur M. Rahman |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
convolution neural networks
medicine.medical_specialty neonatal sepsis Neonatal sepsis tabular data Computer Networks and Communications business.industry Deep learning TK5101-6720 Information technology T58.5-58.64 medicine.disease lstm Computer Science Applications machine learning Telecommunication Computer Science (miscellaneous) medicine Artificial intelligence Neonatal health Electrical and Electronic Engineering business Intensive care medicine Early onset |
| Zdroj: | Journal of Information and Telecommunication, Vol 5, Iss 2, Pp 226-246 (2021) |
| ISSN: | 2475-1847 2475-1839 |
| DOI: | 10.1080/24751839.2020.1843121 |
| Popis: | Neonatal sepsis that is a major threat for maternal and neonatal health worldwide. In this work we design non-invasive, deep learning classification models for predicting accurately and efficiently the early-onset sepsis in neonates in Neonatal Intensive Care Units. By non-invasive, it means that no external instrument or foreign body is introduced when taking data for the classifier. Moreover, the data collected for the purpose of predicting and classifying subjects with neonatal sepsis is in the form of tabular, structured data. The deep learning classification models we design and propose in this are known for working with time series, sequential or image data. Hence, the objective of the current research is to propose such a model that makes use of the powerful tools inherent in Neural Networks for pattern recognition, and use them to outperform traditional machine learning algorithms to detect early-onset neonatal sepsis. Real life neonatal sepsis data samples from two different hospitals are used (Crecer’s Hospital Centre in Cartagena-Colombia and Children’s Hospital of Philadelphia) to make the evaluation of the Neural Networks as authentic as possible. |
| Databáze: | OpenAIRE |
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