RT Journal Article
T1 Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis
A1 Vilar Varela, Santiago
A1 Harpaz, Rave
A1 Santana Penín, María Lourdes
A1 Uriarte Villares, Eugenio
A1 Friedman, Carol
AB BackgroundAdverse drug events (ADEs) detection and assessment is at the center of pharmacovigilance. Data mining of systems, such as FDA’s Adverse Event Reporting System (AERS) and more recently, Electronic Health Records (EHRs), can aid in the automatic detection and analysis of ADEs. Although different data mining approaches have been shown to be valuable, it is still crucial to improve the quality of the generated signals.ObjectiveTo leverage structural similarity by developing molecular fingerprint-based models (MFBMs) to strengthen ADE signals generated from EHR data.MethodsA reference standard of drugs known to be causally associated with the adverse event pancreatitis was used to create a MFBM. Electronic Health Records (EHRs) from the New York Presbyterian Hospital were mined to generate structured data. Disproportionality Analysis (DPA) was applied to the data, and 278 possible signals related to the ADE pancreatitis were detected. Candidate drugs associated with these signals were then assessed using the MFBM to find the most promising candidates based on structural similarity.ResultsThe use of MFBM as a means to strengthen or prioritize signals generated from the EHR significantly improved the detection accuracy of ADEs related to pancreatitis. MFBM also highlights the etiology of the ADE by identifying structurally similar drugs, which could follow a similar mechanism of action.ConclusionThe method proposed in this paper provides evidence of being a promising adjunct to existing automated ADE detection and analysis approaches.
PB PLOS
YR 2012
FD 2012
LK http://hdl.handle.net/10347/22255
UL http://hdl.handle.net/10347/22255
LA eng
NO Vilar S, Harpaz R, Santana L, Uriarte E, Friedman C (2012) Enhancing Adverse Drug Event Detection in Electronic Health Records Using Molecular Structure Similarity: Application to Pancreatitis. PLoS ONE 7(7): e41471. https://doi.org/10.1371/journal.pone.0041471
NO This work was supported by grants R01 LM010016 (Dr. Friedman), R01 LM010016-0S1 (Dr. Friedman), R01 LM010016-0S2 (Dr. Friedman), R01 LM008635(Dr. Friedman), and T15 LM007079 (Dr. Harpaz) from the National Library of Medicine, “Plan Galego de Investigación, Innovación e Crecemento 2011–2015 (I2C)”, European Social Fund (ESF) and Angeles Alvariño program from Xunta de Galicia (Spain)
DS Minerva
RD 27 abr 2026