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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...
Uloženo v:
| Vydáno v: | BMC Med Inform Decis Mak |
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| Hlavní autoři: | , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
BioMed Central
2016
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4965710/ https://ncbi.nlm.nih.gov/pubmed/27459993 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12911-016-0311-6 |
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