Medical Time-series Prediction With LSTM-MDN-ATTN

被引：0

作者：

Park, Hwin Dol ^{[1
]}

Han, Youngwoong ^{[1
]}

Choi, Jae Hun ^{[1
]}

机构：

[1] Elect & Telecommun Res Inst, Med Informat Res Sect, Daejeon, South Korea

来源：

2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE | 2019年

关键词：

electronics medical records; mixture density networks; recurrent neural networks; time-series regression;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We introduce the LSTM-MDN-ATTN model for predicting the medical time-series data. The LSTM-MDN-ATTN model predicts the future value of medical data by approximating the distribution of target data. Since medical data is multivariate data with various test items, attention mechanism is used to model the distribution suitable for target data. The attention layer used in this study predicts target data by focusing on the distribution that is related to the target data. The proposed LSTM-MDN-ATTN model shows better results compared to baseline models using lab test data from Asan Medical Center in Seoul.

引用

下载

页码：1359 / 1361

页数：3

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