-
Je něco špatně v tomto záznamu ?
The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature
M. Salas, J. Petracek, P. Yalamanchili, O. Aimer, D. Kasthuril, S. Dhingra, T. Junaid, T. Bostic
Jazyk angličtina Země Nový Zéland
Typ dokumentu systematický přehled
NLK
ProQuest Central
od 2008-05-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2008-05-01 do Před 1 rokem
- MeSH
- farmakovigilance * MeSH
- léčivé přípravky MeSH
- lidé MeSH
- nežádoucí účinky léčiv * epidemiologie MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- systematický přehled MeSH
INTRODUCTION: Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE: The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS: Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS: Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION: Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
Daiichi Sankyo Inc and Rutgers University 211 Mount Airy Rd Basking Ridge NJ USA
Institute of Pharmacovigilance Hvezdova 2b 14000 Prague Czech Republic
Labcorp Drug Development Princeton NJ USA
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc22024310
- 003
- CZ-PrNML
- 005
- 20221031100455.0
- 007
- ta
- 008
- 221017s2022 nz f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1007/s40290-022-00441-z $2 doi
- 035 __
- $a (PubMed)35904529
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a nz
- 100 1_
- $a Salas, Maribel $u Daiichi Sankyo, Inc. & Center for Real-World Effectiveness and Safety of Therapeutics (CREST), University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 211 Mount Airy Rd, Basking Ridge, NJ, USA $1 https://orcid.org/0000000156341558
- 245 14
- $a The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature / $c M. Salas, J. Petracek, P. Yalamanchili, O. Aimer, D. Kasthuril, S. Dhingra, T. Junaid, T. Bostic
- 520 9_
- $a INTRODUCTION: Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE: The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS: Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS: Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION: Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
- 650 _2
- $a umělá inteligence $7 D001185
- 650 12
- $a nežádoucí účinky léčiv $x epidemiologie $7 D064420
- 650 _2
- $a lidé $7 D006801
- 650 _2
- $a strojové učení $7 D000069550
- 650 _2
- $a léčivé přípravky $7 D004364
- 650 12
- $a farmakovigilance $7 D060735
- 655 _2
- $a systematický přehled $7 D000078182
- 700 1_
- $a Petracek, Jan $u Institute of Pharmacovigilance, Hvezdova 2b, 14000, Prague, Czech Republic $1 https://orcid.org/0000000308060879
- 700 1_
- $a Yalamanchili, Priyanka $u Daiichi Sankyo, Inc. & Rutgers University, 211 Mount Airy Rd, Basking Ridge, NJ, USA. pyalamanchi@dsi.com $1 https://orcid.org/0000000252908665
- 700 1_
- $a Aimer, Omar $u Innovigilance, Laval, QC, Canada $1 https://orcid.org/000000020874486X
- 700 1_
- $a Kasthuril, Dinesh $u Labcorp Drug Development, Princeton, NJ, USA
- 700 1_
- $a Dhingra, Sameer $u Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, India $1 https://orcid.org/0000000325378889
- 700 1_
- $a Junaid, Toluwalope $u Syneos Health, Morrisville, NC, USA $1 https://orcid.org/0000000330974159
- 700 1_
- $a Bostic, Tina $u PPD, part of Thermo Fisher Scientific, Wilmington, NC, USA $1 https://orcid.org/000000017358851X
- 773 0_
- $w MED00209774 $t Pharmaceutical medicine $x 1179-1993 $g Roč. 36, č. 5 (2022), s. 295-306
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/35904529 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y p $z 0
- 990 __
- $a 20221017 $b ABA008
- 991 __
- $a 20221031100453 $b ABA008
- 999 __
- $a ok $b bmc $g 1854182 $s 1175600
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2022 $b 36 $c 5 $d 295-306 $e 20220729 $i 1179-1993 $m Pharmaceutical medicine $n Pharmaceut Med $x MED00209774
- LZP __
- $a Pubmed-20221017