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Pavlovian Conditioned Approach Training in Rats

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Published on: February 4, 2016

Pre-training phenotyping classifiers.

Dmitriy Dligach¹, Majid Afshar², Timothy Miller³

¹Loyola University Chicago, Department of Computer Science, Chicago, IL, United States.

Journal of Biomedical Informatics

|December 1, 2020

Summary

This summary is machine-generated.

We developed a self-supervised pre-training method for transformer models that overcomes input length limitations in clinical text classification. This approach maintains most performance gains compared to supervised methods, even with slightly lower scores.

Keywords:

Automatic phenotyping Natural language processing Pre-training

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Area of Science:

Natural Language Processing
Clinical Informatics
Machine Learning

Background:

Transformer-based pre-trained language models are standard for text classification.
Their clinical application is limited by input length constraints for encounter/patient-level tasks.

Purpose of the Study:

Introduce a self-supervised pre-training method for clinical text classification.
Address the input length limitation of transformer models.
Compare self-supervised with supervised pre-training methods.

Main Methods:

Developed a self-supervised pre-training approach using a masked token objective.
Pre-training method is free from maximum input length limitations.
Compared performance against supervised pre-training using billing codes.

Main Results:

Evaluated on four datasets (one public, three in-house) using AUC and F1 score.
Self-supervised pre-training performed slightly worse than supervised pre-training.
The proposed method retained a significant portion of the performance benefits from pre-training.

Conclusions:

Self-supervised pre-training is a viable alternative for clinical text classification.
The method effectively handles long clinical texts without input length restrictions.
It preserves substantial performance improvements, making it suitable for clinical applications.