<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The increasing application of medical robots in the healthcare sector underscores the critical importance of intent recognition in enhancing the interaction and assistance capabilities of these robots. Traditional intent recognition methods utilize convolutional neural networks (CNNs) for text analysis but often fall short in capturing global features, resulting in incomplete information. To address this challenge, this paper introduces an innovative approach by combining an enhanced CNN with bidirectional gated recurrent units (BiGRU) to construct a dual-channel short-text intent recognition model. This model effectively leverages both local and global features to more accurately comprehend user needs and intentions. Experimental results demonstrate that this model excels, achieving an accuracy rate of 96.68% and an F1 score of 96.67% on the THUCNews_Title dataset. In comparison to conventional intent recognition models, it exhibits significantly improved performance, thereby providing substantial support for medical robots in patient care and assisting healthcare professionals.</p>
</abstract>

The increasing application of medical robots in the healthcare sector underscores the critical importance of intent recognition in enhancing the interaction and assistance capabilities of these robots. Traditional intent recognition methods utilize convolutional neural networks (CNNs) for text analysis but often fall short in capturing global features, resulting in incomplete information. To address this challenge, this paper introduces an innovative approach by combining an enhanced CNN with bidirectional gated recurrent units (BiGRU) to construct a dual-channel short-text intent recognition model. This model effectively leverages both local and global features to more accurately comprehend user needs and intentions. Experimental results demonstrate that this model excels, achieving an accuracy rate of 96.68% and an F1 score of 96.67% on the THUCNews_Title dataset. In comparison to conventional intent recognition models, it exhibits significantly improved performance, thereby providing substantial support for medical robots in patient care and assisting healthcare professionals.

Research on Improved Dual Channel Medical Short Text Intention Recognition Algorithm

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The increasing application of medical robots in the healthcare sector underscores the critical importance of intent recognition in enhancing the interaction...

Model	Acc%	Pre%	Recall%	F1%
SAttBiGRU	96.16	96.20	96.16	96.17
Self-Attention-CNN	94.85	94.89	94.85	94.85
BiGRU-MCNN	95.43	95.45	95.43	95.43
MC-AttCNN-AttBiGRU	95.93	95.98	95.93	95.93

Name	Training Set	Test Set	Validation Set	Category	Total
KUAKE-QIC	6931	1994	1955	11	10880
THUCNews_Title	180000	10000	10000	10	200000

Model	Acc%	Pre%	Recall%	F1%
TextCNN	89.96	89.90	89.96	89.90
Improved TextCNN	94.85	94.89	94.85	94.85
BiGRU-att	94.00	94.17	94.00	94.90
AB-CNN-BGRU-att	96.68	96.68	96.67	96.67

Network Layer	Average Duration	Total Duration	Acc%	F1%
BiGRU-att	2286.9s	45738s	90.83	90.64
BiLSTM-att	2422.55s	48451s	90.45	90.41

Research on Improved Dual Channel Medical Short Text Intention Recognition Algorithm

Online veröffentlicht: 16. März 2024

Seitenbereich: 82 - 89

DOI: https://doi.org/10.2478/ijanmc-2023-0079

Schlüsselwörter
Intention Recognition, Albert, Bigru, Dual Channel

© 2023 Chao Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

comparison of experimental results

experimental dataset

results of ablation experiment

comparison between bigru-att and bilstm-att

Research on Improved Dual Channel Medical Short Text Intention Recognition Algorithm

Online veröffentlicht: 16. März 2024

Seitenbereich: 82 - 89

DOI: https://doi.org/10.2478/ijanmc-2023-0079

SchlüsselwörterIntention Recognition, Albert, Bigru, Dual Channel

© 2023 Chao Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

comparison of experimental results

experimental dataset

results of ablation experiment

comparison between bigru-att and bilstm-att

Schlüsselwörter
Intention Recognition, Albert, Bigru, Dual Channel