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Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and

David Oniani¹, Premkumar Chandrasekar¹, Sonish Sivarajkumar²

¹Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States.

|June 14, 2024

Summary

This summary is machine-generated.

Siamese neural networks (SNNs) show promise for few-shot clinical natural language processing (NLP) tasks. SNN-based methods outperform GPT-2 in low-data scenarios, improving recall and F scores in clinical NLP.

Keywords:

FSL NLP SNN Siamese neural network few-shot learning natural language processing neural networks

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

Area of Science:

Computational linguistics
Artificial intelligence in healthcare
Machine learning for clinical applications

Background:

Natural Language Processing (NLP) is crucial for leveraging electronic health record data.
Deep learning models excel in clinical NLP but require large annotated datasets, which are scarce.
Few-shot learning (FSL) addresses data scarcity, with Siamese Neural Networks (SNNs) showing potential but underexplored in clinical NLP.

Purpose of the Study:

To propose and evaluate Siamese Neural Network (SNN)-based approaches for few-shot clinical NLP tasks.
To investigate the efficacy of SNNs in scenarios with limited annotated clinical data.

Main Methods:

Developed two SNN-based FSL approaches: pretrained SNN and SNN with second-order embeddings.
Evaluated approaches on clinical sentence classification using 4-shot, 8-shot, and 16-shot settings.
Benchmarked against Bidirectional Encoder Representations from Transformers (BERT), BioBERT, BioClinicalBERT, and Generative Pretrained Transformer 2 (GPT-2).

Main Results:

In 4-shot tasks, GPT-2 showed higher precision, but SNNs (BioBERT-based pretrained) achieved better recall and F scores.
SNN-based approaches consistently outperformed GPT-2 in precision, recall, and F score for 8-shot and 16-shot settings.
Demonstrated superior performance of SNNs over prompt-based GPT-2 in low-data clinical NLP.

Conclusions:

The proposed SNN approaches are effective for few-shot clinical NLP tasks.
SNNs offer a viable solution for improving deep learning model performance with limited annotated clinical data.