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Related Experiment Video

Updated: Oct 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical

Avijit Mitra¹, Bhanu Pratap Singh Rawat¹, David D McManus²

¹College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States.

JMIR Medical Informatics

|July 13, 2021

Summary

This summary is machine-generated.

Deep learning models, particularly BERT, effectively extract bleeding event relations from electronic health records, outperforming other methods for improved clinical data analysis.

Keywords:

BERT CNN GCN bleeding electronic health records relation classification

Related Experiment Videos

Last Updated: Oct 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Natural Language Processing (NLP)
Deep Learning (DL)
Clinical Informatics

Background:

Accurate identification of bleeding events in electronic health records (EHRs) is vital for patient care.
Extracting relationships between bleeding events and clinical entities (e.g., anatomic sites, lab tests) is essential for EHR data analysis.
Previous studies have explored NLP and DL for clinical applications, but none have specifically focused on extracting bleeding event-related entity relationships using DL.

Purpose of the Study:

To evaluate the performance of multiple deep learning (DL) systems for classifying relationships between bleeding events and related clinical entities within a novel EHR dataset.
To compare state-of-the-art DL architectures, including CNN, AGGCN, and BERT-based models, on this specific clinical NLP task.

Main Methods:

A new dataset of 1046 deidentified EHR notes was annotated by experts for bleeding events and their attributes.
Three DL architectures were evaluated: Convolutional Neural Network (CNN), Attention-Guided Graph Convolutional Network (AGGCN), and Bidirectional Encoder Representations from Transformers (BERT).
Three BERT variants were tested: BioBERT, Bio+Clinical BERT, and EHR notes-pretrained EHRBERT.

Main Results:

BERT-based models demonstrated significantly superior performance compared to CNN and AGGCN models.
BioBERT achieved the highest macro F1 score of 0.842, outperforming AGGCN by 1.4% and CNN by 7.9%.
The results highlight the effectiveness of BERT models in capturing complex relationships within clinical text.

Conclusions:

BERT-based models are highly effective for classifying relations between bleeding events and other medical concepts in EHRs.
These models outperform traditional DL architectures like CNN and AGGCN for this specific task.
The study suggests that leveraging pretrained contextualized word representations and target entity representations enhances the performance of DL models in clinical NLP.