Cargando…
Treatment effect prediction with adversarial deep learning using electronic health records
BACKGROUND: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption...
Autores principales: | Chu, Jiebin, Dong, Wei, Wang, Jinliang, He, Kunlun, Huang, Zhengxing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735418/ https://www.ncbi.nlm.nih.gov/pubmed/33317502 http://dx.doi.org/10.1186/s12911-020-01151-9 |
Ejemplares similares
-
Evidential MACE prediction of acute coronary syndrome using electronic health records
por: Hu, Danqing, et al.
Publicado: (2019) -
The application of unsupervised deep learning in predictive models using electronic health records
por: Wang, Lei, et al.
Publicado: (2020) -
Predicting hypertension onset from longitudinal electronic health records with deep learning
por: Datta, Suparno, et al.
Publicado: (2022) -
Scalable and accurate deep learning with electronic health records
por: Rajkomar, Alvin, et al.
Publicado: (2018) -
Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors Over Time(*)
por: Yin, Liangying, et al.
Publicado: (2017)