Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data

被引：0

作者：

Guillaume L. Martin

Julien Jouganous

Romain Savidan

Axel Bellec

Clément Goehrs

Mehdi Benkebil

Ghada Miremont

Joëlle Micallef

Francesco Salvo

Antoine Pariente

Louis Létinier

机构：

[1] Synapse Medicine,Département de Santé Publique

[2] Sorbonne Université,undefined

[3] INSERM,undefined

[4] Institut Pierre Louis d’Epidémiologie et de Santé Publique,undefined

[5] AP-HP,undefined

[6] Hôpital Pitié Salpêtrière,undefined

[7] Surveillance Division,undefined

[8] Agence Nationale de Sécurité du Médicament et des Produits de Santé (ANSM),undefined

[9] University of Bordeaux,undefined

[10] INSERM,undefined

[11] BPH,undefined

[12] U1219,undefined

[13] Team Pharmacoepidemiology,undefined

[14] CHU de Bordeaux,undefined

[15] Pôle de Santé Publique,undefined

[16] Service de Pharmacologie Médicale,undefined

[17] Centre de Pharmacovigilance de Bordeaux,undefined

[18] CRPV Marseille Provence Corse,undefined

[19] Service Hospitalo-Universitaire de Pharmacologie Clinique et Pharmacovigilance,undefined

[20] Assistance Publique Hôpitaux de Marseille,undefined

[21] Aix Marseille Université,undefined

[22] Institut des Neurosciences des Systèmes,undefined

[23] INSERM 1106,undefined

来源：

Drug Safety | 2022年 / 45卷

关键词：

D O I：

暂无

中图分类号：

学科分类号：

摘要：

引用

页码：535 / 548

页数：13

共 17 条

[1] Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data
Martin, Guillaume L.
Jouganous, Julien
Savidan, Romain
Bellec, Axel
Goehrs, Clement
Benkebil, Mehdi
Miremont, Ghada
Micallef, Joelle
Salvo, Francesco
Pariente, Antoine
Letinier, Louis
[J]. DRUG SAFETY, 2022, 45 (05) : 535 - 548
[2] Automated Drug Coding Using Artificial Intelligence: An Evaluation of WHODrug Koda on Adverse Event Reports
Meldau, Eva-Lisa
Bista, Shachi
Rofors, Emma
Gattepaille, Lucie M.
[J]. DRUG SAFETY, 2022, 45 (05) : 549 - 561
[3] Automated Drug Coding Using Artificial Intelligence: An Evaluation of WHODrug Koda on Adverse Event Reports
Eva-Lisa Meldau
Shachi Bista
Emma Rofors
Lucie M. Gattepaille
[J]. Drug Safety, 2022, 45 : 549 - 561
[4] Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions
Letinier, Louis
Jouganous, Julien
Benkebil, Mehdi
Bel-Letoile, Alicia
Goehrs, Clement
Singier, Allison
Rouby, Franck
Lacroix, Clemence
Miremont, Ghada
Micallef, Joelle
Salvo, Francesco
Pariente, Antoine
[J]. CLINICAL PHARMACOLOGY & THERAPEUTICS, 2021, 110 (02) : 392 - 400
[5] Adverse Drug Reaction Mining in Pharmacovigilance Data Using Formal Concept Analysis
Villerd, Jean
Toussaint, Yannick
Louet, Agnes Lillo-Le
[J]. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT III, 2010, 6323 : 386 - 401
[6] Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data
Seunghee Lee
Hyekyung Woo
Chung Chun Lee
Gyeongmin Kim
Jong-Yeup Kim
Suehyun Lee
[J]. Scientific Reports, 13
[7] Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data
Lee, Seunghee
Woo, Hyekyung
Lee, Chung Chun
Kim, Gyeongmin
Kim, Jong-Yeup
Lee, Suehyun
[J]. SCIENTIFIC REPORTS, 2023, 13 (01)
[8] A Real-World Data Driven Pharmacovigilance Investigation on Drug-Induced Arrhythmia Using KAERS DB, a Korean Nationwide Adverse Drug Reporting System
Go, Chaerin
Kim, Semi
Kim, Yujin
Sunwoo, Yongjun
Eom, Sae Hyun
Yun, Jiseong
Shin, Sooyoung
Choi, Yeo Jin
Owczuk, Radoslaw
[J]. PHARMACEUTICALS, 2023, 16 (11)
[9] Machine learning and semantic information for unstructured healthcare data: Comparison of methods through the automatic analysis of adverse drug reaction reports
Letinier, Louis
Jouganous, Julien
Miremont, Ghada
Bel-Letoile, Alicia
Salvo, Francesco
Rouby, Franck
Micallef, Joelle
Benkebil, Mehdi
Pariente, Antoine
[J]. PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2020, 29 : 328 - 328
[10] Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan
Kanda, Eiichiro
Epureanu, Bogdan I.
Adachi, Taiji
Tsuruta, Yuki
Kikuchi, Kan
Kashihara, Naoki
Abe, Masanori
Masakane, Ikuto
Nitta, Kosaku
[J]. PLOS ONE, 2020, 15 (05):

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