Representation learning for clinical time series prediction tasks in electronic health records.
BMC Med Inform Decis Mak
; 19(Suppl 8): 259, 2019 12 17.
Article
em En
| MEDLINE
| ID: mdl-31842854
ABSTRACT
BACKGROUND:
Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error and systematic bias. In particular, temporal information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful.METHOD:
In this paper, we propose a general-purpose patient representation learning approach to summarize sequential EHRs. Specifically, a recurrent neural network based denoising autoencoder (RNN-DAE) is employed to encode inhospital records of each patient into a low dimensional dense vector.RESULTS:
Based on EHR data collected from Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, we experimentally evaluate our proposed RNN-DAE method on both mortality prediction task and comorbidity prediction task. Extensive experimental results show that our proposed RNN-DAE method outperforms existing methods. In addition, we apply the "Deep Feature" represented by our proposed RNN-DAE method to track similar patients with t-SNE, which also achieves some interesting observations.CONCLUSION:
We propose an effective unsupervised RNN-DAE method to summarize patient sequential information in EHR data. Our proposed RNN-DAE method is useful on both mortality prediction task and comorbidity prediction task.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Registros Eletrônicos de Saúde
/
Aprendizado de Máquina
/
Previsões
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article