Learning temporal weights of clinical events using variable importance.
Background: Longitudinal data sources, such as electronic health records (EHRs), are very valuable for monitoring adverse drug events (ADEs). However, ADEs are heavily under-reported in EHRs. Using machine learning algorithms to automatically detect patients that should have had ADEs reported in the...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 16; pp. 111 - 122 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
7/21/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |