
Adverse drug reactions (ADRs) represent a serious worldwide problem. Current post-marketing ADR detection approaches largely rely on spontaneous reports filed by various healthcare professionals such as physicians, pharmacists et.al.. Underreporting is a serious deficiency of these methods - the actually reported adverse events represent less than 10% of all cases. Studies show that two important reasons that cause the underreporting are: 1) healthcare professionals are unaware of encountered ADRs, especially for those unusual ADRs, 2) they are too busy to voluntarily report ADRs since it takes a lot of time to fill out the reporting forms. This paper addresses these two issues by developing a high performance agent-based ADR reporting system. The system can 1) help healthcare professionals detect the causal relationship between a drug and an ADR by analyzing patients' electronic records, 2) make the reporting much easier by automatically linking the patients' electronic data with the reporting form.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
