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Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals
BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate...
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| Pubblicato in: | PLoS One |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Public Library of Science
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6248973/ https://ncbi.nlm.nih.gov/pubmed/30462745 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0207749 |
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