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Drug Safety

, Volume 42, Issue 1, pp 135–146 | Cite as

Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks

  • Bharath Dandala
  • Venkata Joopudi
  • Murthy DevarakondaEmail author
Original Research Article
Part of the following topical collections:
  1. NLP Challenge for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0)

Abstract

Background and Significance

Adverse drug events (ADEs) occur in approximately 2–5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. Using state-of-the-art deep-learning neural networks to jointly model concept and relation extraction, we achieved the highest integrated task score in the 2018 Medication and Adverse Drug Event (MADE) 1.0 challenge.

Methods

We used a combined bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) neural network to detect medical entities relevant to ADEs and a combined BiLSTM and attention network to determine relations, including the adverse drug reaction relation between medication and sign or symptom entities. Using these models, we conducted three experiments: (1) separate and sequential modeling of entities and relations; (2) joint modeling where relations between medications and sign or symptoms determined ADE and indication entities; (3) use of information from external resources such as the US FDA’s adverse event database as additional input to the second method.

Results

Joint modeling improved the overall task accuracy from 0.62 to 0.65 F measure, and the additional use of external resources improved the accuracy to 0.66 F measure. Given the gold-standard medical entity labels, the joint model plus external resources method achieved F measures of 0.83 for ADE-relevant medical entity detection and 0.87 for relation detection.

Conclusion

It is important to use joint modeling techniques and external resources for effectively detecting ADEs from clinical narratives in electronic health record (EHR) systems. While the extraction of entities and relations individually achieved high accuracy, the integrated task still has room for further improvement.

Notes

Compliance with Ethical Standards

Funding

No sources of funding were used to conduct this study or prepare this manuscript.

Approval and consent

This study was conducted on de-identified clinical notes as part of a shared challenge, so no ethical approval or patient consent was required.

Conflict of interest

Bharath Dandala, Venkata Joopudi, and Murthy Devarakonda have no conflicts of interest that are directly relevant to the content of this article. Dr. Devarakonda is now on the faculty in Biomedical Informatics at Arizona State University, USA.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IBM ResearchYorktown HeightsUSA
  2. 2.Biomedical InformaticsArizona State UniversityTempeUSA

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