Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery

Estimation with Quadratic Loss

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Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports

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A Bayesian neural network method for adverse drug reaction signal generation

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Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System

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Selecting the right interestingness measure for association patterns

Tan, P., Kumar, V., & Srivastava, J. (2002) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775053 · Cited by 868

CONCEPTS OF INTERACTION

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Large-Scale Bayesian Logistic Regression for Text Categorization

Genkin, A., Lewis, D. D., & Madigan, D. (2007) · Technometrics · doi:10.1198/004017007000000245 · Cited by 812

VigiBase, the WHO Global ICSR Database System: Basic Facts

Lindquist, M. (2008) · Drug Information Journal · doi:10.1177/009286150804200501 · Cited by 599

Selective serotonin reuptake inhibitors in pregnant women and neonatal withdrawal syndrome: a database analysis

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Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability:

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Sedative co-medication patterns across frailty states in people with HIV: a network-based study

Michael, H. U., Brouillette, M., Tamblyn, R. M., Fellows, L. K., & Mayo, N. E. (2025) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-025-01963-7

Drug‐induced respiratory depression in FDA Adverse Event Reporting System for 20 years

Zhou, W., & Yi, X. (2025) · Hong Kong Journal of Emergency Medicine · doi:10.1002/hkj2.70033

Additional file 3 of Drug-associated hyperammonaemia: a Bayesian analysis of the WHO Pharmacovigilance Database

Balcerac, A., Bihan, K., Lebrun‐Vignes, B., Thabut, D., Salem, J., & Weiss, N. (2022) · Figshare · doi:10.6084/m9.figshare.20098675

Additional file 2 of Drug-associated hyperammonaemia: a Bayesian analysis of the WHO Pharmacovigilance Database

Balcerac, A., Bihan, K., Lebrun‐Vignes, B., Thabut, D., Salem, J., & Weiss, N. (2022) · Figshare · doi:10.6084/m9.figshare.20098672

Additional file 4 of Drug-associated hyperammonaemia: a Bayesian analysis of the WHO Pharmacovigilance Database

Balcerac, A., Bihan, K., Lebrun‐Vignes, B., Thabut, D., Salem, J., & Weiss, N. (2022) · Figshare · doi:10.6084/m9.figshare.20098678

Additional file 1 of The real-world safety profile of sodium-glucose co-transporter-2 inhibitors among older adults (≥ 75 years): a retrospective, pharmacovigilance study

Goldman, A., Fishman, B., Twig, G., Raschi, E., Cukierman‐Yaffe, T., Moshkovits, Y., Pomerantz, A., Ben‐Zvi, I., Dankner, R. S., & Maor, E. (2024) · Figshare · doi:10.6084/m9.figshare.26563896

Additional file 1 of Exploration of the potential association between GLP-1 receptor agonists and suicidal or self-injurious behaviors: a pharmacovigilance study based on the FDA Adverse Event Reporting System database

Zhou, J., Zheng, Y., Xu, B., Long, S., Zhu, L.-E., Liu, Y., Li, C., Zhang, Y., Liu, M., & Wu, X. (2024) · Figshare · doi:10.6084/m9.figshare.26680684

Additional file 1 of Drug-associated hyperammonaemia: a Bayesian analysis of the WHO Pharmacovigilance Database

Balcerac, A., Bihan, K., Lebrun‐Vignes, B., Thabut, D., Salem, J., & Weiss, N. (2022) · Figshare · doi:10.6084/m9.figshare.20098666

Additional file 2 of The real-world safety profile of sodium-glucose co-transporter-2 inhibitors among older adults (≥ 75 years): a retrospective, pharmacovigilance study

Goldman, A., Fishman, B., Twig, G., Raschi, E., Cukierman‐Yaffe, T., Moshkovits, Y., Pomerantz, A., Ben‐Zvi, I., Dankner, R. S., & Maor, E. (2024) · Figshare · doi:10.6084/m9.figshare.26563899

Risk of drug eruption associated with the use of tetracyclines, macrolides, and fluoroquinolones: real-world evidence from a pharmacovigilance study utilizing the FDA adverse event reporting system

Alshammari, T. M. (2026) · Saudi Pharmaceutical Journal · doi:10.1007/s44446-025-00058-8

Real-world study of adverse events associated with ceftazidime/avibactam based on the U.S. Food and Drug Administration adverse event reporting system database

Zheng, X., Yang, P., Ou, Y.-X., You, B.-B., Zhang, L., Cheng, Y., Cheng, M., & Xiang, P. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1698293

Unraveling plastome evolution in Plectranthus barbatus : comparative genomics, repeat dynamics and phylogenetic insights across related species

Goswami, M. M., Bhatt, P. P., Patel, V. J., Solanki, H., & Thaker, V. S. (2026) · Journal of Mitochondria Plastids and Endosymbiosis · doi:10.1080/28347056.2026.2640828

A systemic pharmacovigilance assessment of ophthalmic atropine: signal detection and clinical prioritization from the FAERS database

Xu, Z., Liang, J., Cai, W., Luo, Y., Liang, F., Lin, R., Hu, C., Su, T., Wu, Y., He, J., et al. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1798521

Comparative analysis of French and Vietnamese pharmacovigilance databases with pharmacoepidemiological application and improvement of the underreporting of adverse drug reactions in Vietnam

Nguyen, K. D. (2018) · doi:10.70675/853b832dze867z4e06za273zea2ecbfe5a40

Multimodal investigation of the association between bevacizumab and pulmonary arterial hypertension

Valentin, S., Hlavaty, A., Brossat, C., Chaumais, M., Artaud-Macari, É., Mauran, P., Maurac, A., Savale, L., Sitbon, O., Humbert, M. J. C., et al. (2026) · ERJ Open Research · doi:10.1183/23120541.00209-2026

An updated pharmacovigilance evidence from U.S. tobacco problem reports following the FDA safety communication on e-cigarettes and seizures

Azzolina, D., Bartolotta, P., Murri, M. B., & Gregori, D. (2026) · BMC Public Health · doi:10.1186/s12889-026-27386-1

Metoclopramide and Risk of Pheochromocytoma Crisis: A Causality Assessment Using Literature and VigiBase Data

Bahta, M., Dawit, A., Okubazghi, M., & Russom, M. (2026) · Drug Healthcare and Patient Safety · doi:10.2147/dhps.s593970

Comparative thromboembolic risk and polypharmacy-related drug–drug interaction signals among antidiabetic medications: a large-scale FAERS pharmacovigilance study

Bafail, D. A., & Ghazal, H. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1826021

HIV resistance to integrase strand-transfer inhibitors: disproportionality analysis of individual case safety reports from the WHO global safety database (VigiBase)

Basílio, H. S., Reis, A. M. M., Braga, L. P., Fonseca, L. B., Drummond, P. L. D. M., Malta, J. S., & de Pádua, C. A. M. (2026) · Current Medical Research and Opinion · doi:10.1080/03007995.2026.2682588

Comment on Nie et al (2026) ‘Suicidality reports in acne patients treated with isotretinoin and concomitant antidepressants: A descriptive analysis of FDA Adverse Event Reporting System Data'

Li, D., Zhou, J. S., & Zheng, X. (2026) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2026.05.137

Chemical Information Modeling for Component-Level Review Prioritization of Adverse-Reaction Signals in Multi-Component Medicines

Shi, B., Zhang, X., Wu, J., Fang, J., Chen, J. Y., & Yang, J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01464

Real-world safety profile of mitotane in adrenocortical carcinoma: a retrospective cross-sectional study integrating pharmacovigilance and interpretable machine learning

Wang, J., Yang, Y., Xu, K., Hou, N., & Guo, C. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1862262

Neuropsychiatric adverse event reporting with GLP-1-based therapies: a post-marketing pharmacovigilance analysis using the WHO global individual case safety report database (VigiBase)

Liang, J., Bo, L., Miao, X., Guo, X., Xu, J., He, J., & Ye, X. (2026) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-026-01190-4

Beyond disproportionality: systematic prioritization of actionable levothyroxine safety signals in FAERS

Baskaran, B. S., Chalabianloo, N., Eddin, L. E., & Muanda, F. T. (2026) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/cjpp-2025-0331

Identifying Cardiac Adverse Event Signals for Natural Products Using Disproportionality Analysis of the FDA Adverse Event Reporting System (FAERS)

Kazarov, C. L., Boyce, R. D., Taneja, S. B., Abanyie, K., Sestito, M. R., & Kane‐Gill, S. L. (2026) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1155/jcpt/8979418

Drug Safety and Polypharmacy Signals in Ibrutinib-Treated Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma: Age- and Sex-Specific FAERS Analysis of CYP3A Modifier and Antithrombotic Co-Exposure

Shivarov, V. S. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15166370

Differential fracture risk among proton pump inhibitors in older adults: evidence network and pharmacovigilance validation

Piao, H.-L., Wu, Z.-H., Zhou, L., Lai, C.-H., Tang, Z.-Y., Yin, W., & Yi, Y.-H. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1835602

Cardiovascular Adverse Event Reporting Patterns for Atenolol, Bisoprolol, and Nebivolol: A Pharmacovigilance Disproportionality Analysis of the FDA Adverse Event Reporting System

Nho, M., Kumar, V., Vathy, H., Prakash, R., Abdel‐Latif, A., & Singh, A. P. (2026) · Cardiovascular Medicine · doi:10.3390/cardiovascmed29040041

A real-world disproportionality analysis of adverse events associated with lenvatinib in thyroid cancer patients based on the FAERS databasePreprint

Zeng, X., & ZHANG, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10081682/v1

Haemorrhagic event reporting with concomitant gemcitabine and warfarin: a VigiBase disproportionality analysis

Nishida, K., Chrétien, B., Silva, A. D., Calabia, A., Matsui, S., Terada, T., & Ando, Y. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1858594

BRAF/MEK Inhibitor–Associated Stevens–Johnson Syndrome/Toxic Epidermal Necrolysis: A Systematic Review and an FDA Pharmacovigilance Database Study

Zhou, J., Luo, J., Zhu, J., Lin, X., Yang, K., Chen, X., Li, S., & Jiang, J. (2026) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1155/jcpt/7444836

Masking effect in the Spanish spontaneous reporting database FEDRA

Cueto, N. S., Gutiérrez-Lobón, M., Ventura, A. N., & Fernández‐Fernández, C. (2026) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-026-04185-6

Male Reproductive Disorders Associated with Antiseizure Medications: Evidence from Meta-Analysis and FAERS Pharmacovigilance Study

Na, H., Choi, B., Oh, S.-H., Jo, I.-J., Kim, D. K., & Song, Y. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15197566

Hallucinations associated with bilastine in a teenager: case report and disproportionality analysis in VigiBase®

Ewig, E., Ben-Othman, N., Viard, D., Rocher, F., & Gérard, A. O. (2026) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-026-03212-2

A Distinct Delayed-Onset Intraocular Inflammation Phenotype of Faricimab versus Aflibercept and Ranibizumab: Triangulated Pharmacovigilance Evidence from Databases of US, Europe and JapanPreprint

Jiang, J., Yifan, G., Hu, D., Li, X., Qin, S., Ling, Q., Zhang, Q., Dong, L., & Lin, Z. (2026) · medRxiv · doi:10.64898/2026.10.10.26365218

Adverse Event Reporting Patterns of Tobacco Cessation Medications in Korea: A Disproportionality Analysis

Min, J., 조은아, Shin, S., & Choi, Y. J. (2026) · Healthcare · doi:10.3390/healthcare14203479

Glucocorticoid-centered drug spectrum of non-jaw osteonecrosis, a FAERS pharmacovigilance study with independent validation in Canada Vigilance and JADER

戚力升, Qiu, Y., Wang, X., Zhang, T., & Jiang, H. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1977191

Cutaneous reporting patterns of drugs relevant to hepatocellular carcinoma: a cross-indication FAERS analysis and proposed management framework

Sun, X., Liu, Z., Zhang, J., Zhai, L., & Yu, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1959163

Temporal pattern discovery for trends and transient effects

Norén, G. N., Bate, A., Hopstadius, J., Star, K., & Edwards, I. R. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1402005 · Cited by 47

Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database

CasterOla, Niklas, N., MadiganDavid, & BateAndrew (2010) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.5555/1855228.1855230

Temporal pattern discovery in longitudinal electronic patient records

Norén, G. N., Hopstadius, J., Bate, A., Star, K., & Edwards, I. R. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0152-3 · Cited by 170

Efficient discovery of risk patterns in medical data

Li, J., Fu, A. W.-C., & Fahey, P. P. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.07.008 · Cited by 54

Significance of Patterns in Data Visualisations

Savvides, R., Henelius, A., Oikarinen, E., & Puolamäki, K. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330994 · Cited by 10