Clinicians’ Reports in Electronic Health Records Versus Patients’ Concerns in Social Media: A Pilot Study of Adverse Drug Reactions of Aspirin and Atorvastatin

This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text. The class imbalance was tackled using different sampling methods, cost-sensitive learning, ensemble learning and one-class classification and the Random Forest classifier was used. The adverse drug reaction extraction model was inferred from a dataset that comprises real electronic health records with an imbalance ratio of 1:222, this means that for each drug–disease pair that is an adverse drug reaction, there are approximately 222 that are not adverse drug reactions. The application of a sampling technique before using cost-sensitive learning offered the best result. On the test set, the f-measure was 0.121 for the minority class and 0.996 for the majority class.

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Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Scientific Reports ◽

10.1038/s41598-018-22521-4 ◽

2018 ◽

Vol 8 (1) ◽

Author(s):

Daniel M. Bean ◽

Honghan Wu ◽

Ehtesham Iqbal ◽

Olubanke Dzahini ◽

Zina M. Ibrahim ◽

...

Keyword(s):

Electronic Health Records ◽

Adverse Drug Reactions ◽

Knowledge Graph ◽

Drug Reactions ◽

Health Records ◽

Electronic Health

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High-throughput framework for genetic analyses of adverse drug reactions using electronic health records

PLoS Genetics ◽

10.1371/journal.pgen.1009593 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1009593

Author(s):

Neil S. Zheng ◽

Cosby A. Stone ◽

Lan Jiang ◽

Christian M. Shaffer ◽

V. Eric Kerchberger ◽

...

Keyword(s):

Electronic Health Records ◽

Precision Medicine ◽

Adverse Drug Reactions ◽

High Throughput ◽

Drug Response ◽

Small Sample ◽

Genome Wide Association Studies ◽

Drug Reactions ◽

Health Records ◽

Electronic Health

Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions (ADRs) using “drug allergy” labels from electronic health records (EHRs). As a proof-of-concept, we conducted GWAS for ADRs to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Center’s BioVU DNA Biobank. We identified 7 genetic loci associated with ADRs at P < 5 × 10−8, including known genetic associations such as CYP2D6 and OPRM1 for CYP2D6-metabolized opioid ADR. Additional expression quantitative trait loci and phenome-wide association analyses added evidence to the observed associations. Our high-throughput framework is both scalable and portable, enabling impactful pharmacogenomic research to improve precision medicine.

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