TEDAR: Temporal dynamic signal detection of adverse reactions
Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The...
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| Vydáno v: | Artificial intelligence in medicine Ročník 122; s. 102212 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.12.2021
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| ISSN: | 0933-3657, 1873-2860, 1873-2860 |
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| Abstract | Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.
•Detection of signals in pharmacovigilance datasets is improved by temporal dynamics.•TEDAR detects a greater number of true signals without increasing false positives.•TEDAR detects adverse reactions months before the other methodologies. |
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| AbstractList | Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length. Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length. Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length. •Detection of signals in pharmacovigilance datasets is improved by temporal dynamics.•TEDAR detects a greater number of true signals without increasing false positives.•TEDAR detects adverse reactions months before the other methodologies. |
| ArticleNumber | 102212 |
| Author | Bonnici, Vincenzo Giugno, Rosalba Sala, Pietro Aparo, Antonino |
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| Cites_doi | 10.1002/9781119413431.ch10 10.3390/ijerph16122221 10.1002/pds.4672 10.1007/s40264-015-0345-1 10.1002/pds.4184 10.3390/life10080138 10.1002/pds.677 10.2165/11534410-000000000-00000 10.1080/00031305.1999.10474456 10.1186/s12911-017-0472-y 10.1002/sim.8447 10.1177/2042098619882819 10.1007/s40264-015-0289-5 10.1093/nar/gku1066 10.1177/0962280216643116 10.1002/pds.1742 10.1002/pds.1001 10.1007/s40264-016-0405-1 10.1007/s002280050466 |
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| Keywords | ESD Dynamic temporal intervals Graph-based algorithm DTI Adverse drug reactions Early signal detection Pharmacovigilance datasets Signal detection ADR |
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| SubjectTerms | Adverse Drug Reaction Reporting Systems Adverse drug reactions Databases, Factual Drug-Related Side Effects and Adverse Reactions - diagnosis Dynamic temporal intervals Early signal detection Graph-based algorithm Humans Pharmacovigilance Pharmacovigilance datasets Signal detection |
| Title | TEDAR: Temporal dynamic signal detection of adverse reactions |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0933365721002050 https://dx.doi.org/10.1016/j.artmed.2021.102212 https://www.ncbi.nlm.nih.gov/pubmed/34823837 https://www.proquest.com/docview/2604016553 |
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