Literature DB >> 30876845

Machine learning on adverse drug reactions for pharmacovigilance.

Chun Yen Lee1, Yi-Ping Phoebe Chen2.   

Abstract

Machine learning, especially deep learning, has the predictive power to predict adverse drug reactions, repurpose drugs and perform precision medicine. We provide a background of machine learning and propose a potential high-performance deep learning framework for its successful applications in these practices.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Mesh：

Year:  2019        PMID： 30876845     DOI： 10.1016/j.drudis.2019.03.003

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN： 1359-6446            Impact factor:   7.851


  10 in total

Review 1.  Machine Learning in Causal Inference: Application in Pharmacovigilance.

Authors:  Yiqing Zhao; Yue Yu; Hanyin Wang; Yikuan Li; Yu Deng; Guoqian Jiang; Yuan Luo
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

Review 2.  Intelligent Telehealth in Pharmacovigilance: A Future Perspective.

Authors:  Heba Edrees; Wenyu Song; Ania Syrowatka; Aurélien Simona; Mary G Amato; David W Bates
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

3.  Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

Authors:  Muhammad Imran; Aasia Bhatti; David M King; Magnus Lerch; Jürgen Dietrich; Guy Doron; Katrin Manlik
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

4.  Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.

Authors:  Hae Reong Kim; MinDong Sung; Ji Ae Park; Kyeongseob Jeong; Ho Heon Kim; Suehyun Lee; Yu Rang Park
Journal:  Medicine (Baltimore)       Date:  2022-06-24       Impact factor: 1.817

Review 5.  Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.

Authors:  Lena Davidson; Mary Regina Boland
Journal:  J Pharmacokinet Pharmacodyn       Date:  2020-04-11       Impact factor: 2.745

6.  Prediction of adverse drug reactions based on knowledge graph embedding.

Authors:  Fei Zhang; Bo Sun; Xiaolin Diao; Wei Zhao; Ting Shu
Journal:  BMC Med Inform Decis Mak       Date:  2021-02-04       Impact factor: 2.796

7.  Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system.

Authors:  Jeong-Eun Lee; Ju Hwan Kim; Ji-Hwan Bae; Inmyung Song; Ju-Young Shin
Journal:  Sci Rep       Date:  2022-09-01       Impact factor: 4.996

8.  Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation.

Authors:  Ruoqi Yang; Guiping Zhao; Bin Yan
Journal:  Molecules       Date:  2022-09-22       Impact factor: 4.927

9.  Efficient prediction of drug-drug interaction using deep learning models.

Authors:  Prashant Kumar Shukla; Piyush Kumar Shukla; Poonam Sharma; Paresh Rawat; Jashwant Samar; Rahul Moriwal; Manjit Kaur
Journal:  IET Syst Biol       Date:  2020-08       Impact factor: 1.615

10.  Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Authors:  Ke Liu; Ruo-Fan Ding; Han Xu; Yang-Mei Qin; Qiu-Shun He; Fei Du; Yun Zhang; Li-Xia Yao; Pan You; Yan-Ping Xiang; Zhi-Liang Ji
Journal:  Clin Pharmacol Ther       Date:  2020-02-28       Impact factor: 6.875
  10 in total

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