| Autor: |
Yu-wen Chen, Yu-jie Li, Zhi-yong Yang, Kun-hua Zhong, Li-ge Zhang, Yang Chen, Hong-yu Zhi, Peng Deng, Dan-dan Wang, Jian-teng Gu, Jiao-lin Ning, Kai-zhi Lu, Ju Zhang, Zheng-yuan Xia, Bin Yi |
| Rok vydání: |
2020 |
| Předmět: |
|
| DOI: |
10.21203/rs.3.rs-44310/v1 |
| Popis: |
Background Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods Finally, 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results The Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837(0.824–0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity ( |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
