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Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

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Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning

Aman Jaiswal¹, Alan Katz², Marcello Nesca²

¹Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.

JMIR Medical Informatics

|August 16, 2023

Summary

This summary is machine-generated.

Deep learning models, including BERT, can effectively identify lower back pain risk factors in clinical notes. This approach aids in detecting potential underlying conditions, improving patient care and reducing healthcare burdens.

Keywords:

deep learning electronic medical records lower back pain machine learning natural language processing risk factors semantic textual similarity

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Area of Science:

Artificial Intelligence
Natural Language Processing
Machine Learning

Background:

Lower back pain is a widespread condition causing disability and economic burden.
Advances in AI and NLP offer new avenues for identifying lower back pain risk factors.
Early identification of risk factors is crucial for timely intervention and management.

Purpose of the Study:

To develop a deep learning model for detecting lower back pain risk factors in clinical notes.
To address challenges in using NLP for electronic health record free-text analysis.
To provide recommendations for future research in this domain.

Main Methods:

Manual annotation of clinical notes for six risk factors.
Utilizing semantic textual similarity and regular expressions to enhance data capture.
Employing techniques like downsampling, multi-label classification, and unsupervised pretraining.

Main Results:

Bidirectional Encoder Representations from Transformers (BERT) models significantly improved risk factor identification (15% macro-AUROC).
A BERT-convolutional neural network (CNN) model for longer texts showed further improvement (4% macro-AUROC).
Domain adaptation and multitask learning enhanced model stability and performance.

Conclusions:

Primary care clinical notes require preprocessing for effective free-text analysis.
BERT models are valuable for multi-label classification in identifying lower back pain risk factors.
This AI-driven approach can aid in detecting indications for imaging in lower back pain patients.