Prediction research of cervical cancer clinical events based on recurrent neural network
| Autor: | Huimin Ma, Jilong Cao, Kui Zhao, Yufang Yan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Cervical cancer
Process (engineering) Clinical events Computer science business.industry Treatment process 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre medicine.disease Recurrent neural network 0202 electrical engineering electronic engineering information engineering medicine General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence Time series Representation (mathematics) business computer Word (computer architecture) General Environmental Science |
| Zdroj: | Procedia Computer Science. 183:221-229 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.02.052 |
| Popis: | Through the research on the existing time series prediction technology, most researchers mainly make predictions based on clinical events, without thinking about whether the previous clinical process is standard or not. This paper proposes a two-stage prediction model, RNN-2-DT, based on word vector representation and integrated into the standardized judgment of the diagnosis and treatment process. The model mines standardized clinical mode which is a standard of clinical process, using the method of binary K-means. Meantime, the clinical events prediction model using gated recurrent units (GRU) based on recurrent neural network (RNN) is constructed. The experimental results indicate that, compared with the clinical processes no considering standardized judgments, our model’s recall rate and mean average precision are increased by 7.2% and 4.3%, respectively. |
| Databáze: | OpenAIRE |
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