Representation learning for clinical time series prediction tasks in electronic health records

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 particu...

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Detalles Bibliográficos
Autores principales: Ruan, Tong, Lei, Liqi, Zhou, Yangming, Zhai, Jie, Zhang, Le, He, Ping, Gao, Ju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916209/
https://www.ncbi.nlm.nih.gov/pubmed/31842854
http://dx.doi.org/10.1186/s12911-019-0985-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916209/
https://www.ncbi.nlm.nih.gov/pubmed/31842854
http://dx.doi.org/10.1186/s12911-019-0985-7

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