Application of Deep Neural Networks into Classification in Irregular Time Series Data of Patients with Diffuse Large B-cell Lymphoma
10.11783/j.issn.1002-3674.2024.02.006
- VernacularTitle:深度神经网络在不规则弥漫大B细胞淋巴瘤时间序列数据分类预测中的应用
- Author:
Qiong LI
1
;
Yanbo ZHANG
;
Hongmei YU
Author Information
1. 山西医科大学公共卫生学院卫生统计教研室(030001);重大疾病风险评估山西省重点实验室
- Keywords:
Diffuse large B-cell lymphoma;
Irregular time series data;
Recurrence risk prediction;
Deep neural networks
- From:
Chinese Journal of Health Statistics
2024;41(2):190-193,199
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the classification effect of deep neural networks in irregular time series data,and to predict the recurrence risk of 362 patients with diffuse large B-cell lymphoma(DLBCL)in a hospital in Shanxi from 2014 to 2020.Methods A total of 362 diagnosed DLBCL patients who achieved complete remission after initial chemotherapy were collected retrospectively,and the recurrence risk was predicted within the next two years.First,LASSO regression was used to screen the variables.Then a deep neural network model of irregular time series data based on GRU-ODE-Bayes was constructed and compared with some traditional models and other deep neural network models.Results Among all the models under study,the traditional models do not perform as well as the deep neural network models in classification.The GRU-ODE-Bayes model was the best,with AUC of 0.85,sensitivity of 0.84,specificity of 0.71,and G-means of 0.77.Conclusion Compared with other models,the GRU-ODE-Bayes model can predict the recurrence of DLBCL patients more accurately.It could benefit the individualized treatment for patients and decision-making for physicians.
