Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?


Currently submitted to: JMIR Medical Informatics

Date Submitted: Feb 25, 2020
Open Peer Review Period: Feb 25, 2020 - Apr 21, 2020
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Adverse Drug Event Detection From Clinical Notes in Electronic Health Records: A Joint Approach for Entity and Relation Extraction Based on Knowledge-aware Neural Attentive Deep Learning Models

  • Bharath Dandala; 
  • Venkata Joopudi; 
  • Ching-Huei Tsou; 
  • Jennifer J Liang; 
  • Parthasarathy Suryanarayanan; 



An adverse drug event (ADE) is commonly defined as “an injury resulting from medical intervention related to a drug”. Providing information related to ADEs and alerting caregivers at the point-of-care can reduce the risk of prescription and diagnosis errors, and improve health outcomes. ADEs captured in Electronic Health Records (EHR) structured data, as either coded problems or allergies, are often incomplete leading to underreporting. It is therefore important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain richer documentation of a patient’s adverse drug events. Several natural language processing (NLP) systems were previously proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for automatic extraction of ADEs from clinical notes.


The objective of this study is to improve automatic extraction of ADEs and related information such as drugs and their reason for administration from patient clinical notes.


This research was conducted using discharge summaries from the MIMIC-III database obtained through the National NLP Clinical Challenges (n2c2) annotated with Drugs, drug attributes (Strength, Form, Frequency, Route, Dosage, Duration), Adverse Drug Events, Reasons, and relations between drugs and other entities. We developed a deep learning–based system for extracting these drug–centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations with respect to each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context¬–sensitive meanings of words. The position-attention mechanism amplified benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the FAERS database to improve relation extraction, especially when contextual clues are insufficient.


Our system achieved new state-of-the-art results on the n2c2 dataset, with significant improvements in recognizing the crucial Drug-->Reason (F1 0.650 vs 0.579) and Drug-->ADE (0.490 vs 0.476) relations.


We present a system for extracting drug–centric concepts and relations that outperformed current state-of-the-art results. We show that contextualized embeddings, position-attention mechanism and knowledge graph embeddings effectively improve deep learning–based concept and relation extraction. This study demonstrates the further potential for deep learning–based methods to help extract real world evidence from unstructured patient data for drug safety surveillance.


Please cite as:

Dandala B, Joopudi V, Tsou C, Liang JJ, Suryanarayanan P

Adverse Drug Event Detection From Clinical Notes in Electronic Health Records: A Joint Approach for Entity and Relation Extraction Based on Knowledge-aware Neural Attentive Deep Learning Models

JMIR Preprints. 25/02/2020:18417

DOI: 10.2196/preprints.18417


Download PDF

Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.