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Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

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A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension.

Bin Wang¹, Xuejie Zhang¹, Xiaobing Zhou¹

¹School of Information Science and Engineering, Yunnan University, Kunming 650091, China.

International Journal of Environmental Research and Public Health

|February 26, 2020

Summary

We developed a new Gated Dilated Convolution with Attention (GDCA) model for clinical machine reading comprehension. Our model achieves state-of-the-art results and is 8x faster, improving clinical text analysis.

Keywords:

Gated Dilated Convolution attention mechanism clinical medicine cloze-style machine reading comprehension

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Area of Science:

Medical Informatics
Natural Language Processing
Artificial Intelligence

Background:

Machine comprehension in clinical medicine is valuable but underdeveloped.
Existing cloze-style reading comprehension models are often time-consuming.
There is a need for efficient and effective clinical machine reading comprehension models.

Purpose of the Study:

To address the limitations of existing models in clinical machine reading comprehension.
To propose an efficient and effective model for cloze-style machine reading comprehension in the medical domain.
To improve the performance and speed of clinical machine reading comprehension.

Main Methods:

Development of a novel Gated Dilated Convolution with Attention (GDCA) model.
The GDCA model incorporates a gated dilated convolution module and an attention mechanism.
The model is designed for high parallelism and capturing long-distance dependencies.

Main Results:

The GDCA model achieved state-of-the-art results on the CliCR dataset.
Our model surpassed existing best models across several performance metrics.
The training speed of the GDCA model was 8 times faster than the previous best model.

Conclusions:

The proposed GDCA model offers a significant advancement in clinical machine reading comprehension.
The model demonstrates superior performance and remarkable efficiency.
GDCA holds great potential for practical applications in clinical medicine.