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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2016
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965710/ https://www.ncbi.nlm.nih.gov/pubmed/27459993 http://dx.doi.org/10.1186/s12911-016-0311-6 |