A hybrid medical text classification framework: integrating attentive rule construction and neural network
The main objective of this work is to improve the quality and transparency of the medical text classification solutions. Conventional text classification methods provide users with only a restricted mechanism (based on frequency) for selecting features. In this paper, a three-stage hybrid method com...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/65138/ |
| _version_ | 1848800191889539072 |
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| author | Li, Xiang Cui, Menglin Li, Jingpeng Bai, Ruibin Lu, Zheng Aickelin, Uwe |
| author_facet | Li, Xiang Cui, Menglin Li, Jingpeng Bai, Ruibin Lu, Zheng Aickelin, Uwe |
| author_sort | Li, Xiang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The main objective of this work is to improve the quality and transparency of the medical text classification solutions. Conventional text classification methods provide users with only a restricted mechanism (based on frequency) for selecting features. In this paper, a three-stage hybrid method combining the threshold-gated attentive bi-directional Long Short-Term Memory (ABLSTM) and the regular expression based classifier is proposed for medical text classification tasks. The bi-directional Long Short-Term Memory (LSTM) architecture with an attention layer allows the network to weigh words according to their perceived importance and focus on crucial parts of a sentence. Feature words (or keywords) extracted by ABLSTM model are utilized to guide the regular expression rule construction. Our proposed approach leverages the advantages of both the interpretability of rule-based algorithms and the computational power of deep learning approaches for a production-ready scenario. Experimental results on real-world medical online query data clearly validate the superiority of our system in selecting domain-specific and topic-related features. Results show that the proposed approach achieves an accuracy of 0.89 and an F1-score of 0.92 respectively. Furthermore, our experimentation also illustrates the versatility of regular expressions as a user-level tool for focusing on desired patterns and providing interpretable solutions for human modification. |
| first_indexed | 2025-11-14T20:47:39Z |
| format | Article |
| id | nottingham-65138 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:47:39Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-651382021-05-12T02:54:56Z https://eprints.nottingham.ac.uk/65138/ A hybrid medical text classification framework: integrating attentive rule construction and neural network Li, Xiang Cui, Menglin Li, Jingpeng Bai, Ruibin Lu, Zheng Aickelin, Uwe The main objective of this work is to improve the quality and transparency of the medical text classification solutions. Conventional text classification methods provide users with only a restricted mechanism (based on frequency) for selecting features. In this paper, a three-stage hybrid method combining the threshold-gated attentive bi-directional Long Short-Term Memory (ABLSTM) and the regular expression based classifier is proposed for medical text classification tasks. The bi-directional Long Short-Term Memory (LSTM) architecture with an attention layer allows the network to weigh words according to their perceived importance and focus on crucial parts of a sentence. Feature words (or keywords) extracted by ABLSTM model are utilized to guide the regular expression rule construction. Our proposed approach leverages the advantages of both the interpretability of rule-based algorithms and the computational power of deep learning approaches for a production-ready scenario. Experimental results on real-world medical online query data clearly validate the superiority of our system in selecting domain-specific and topic-related features. Results show that the proposed approach achieves an accuracy of 0.89 and an F1-score of 0.92 respectively. Furthermore, our experimentation also illustrates the versatility of regular expressions as a user-level tool for focusing on desired patterns and providing interpretable solutions for human modification. 2021-03-10 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/65138/1/A%20hybrid%20medical%20text%20classification%20framework%EF%BC%9A%20Integrating%20attentive%20rule%20construction%20and%20neural%20network.pdf Li, Xiang, Cui, Menglin, Li, Jingpeng, Bai, Ruibin, Lu, Zheng and Aickelin, Uwe (2021) A hybrid medical text classification framework: integrating attentive rule construction and neural network. Neurocomputing, 443 . pp. 345-355. ISSN 09252312 hybrid system; deep learning; attention mechanism; text classification http://dx.doi.org/10.1016/j.neucom.2021.02.069 doi:10.1016/j.neucom.2021.02.069 doi:10.1016/j.neucom.2021.02.069 |
| spellingShingle | hybrid system; deep learning; attention mechanism; text classification Li, Xiang Cui, Menglin Li, Jingpeng Bai, Ruibin Lu, Zheng Aickelin, Uwe A hybrid medical text classification framework: integrating attentive rule construction and neural network |
| title | A hybrid medical text classification framework: integrating attentive rule construction and neural network |
| title_full | A hybrid medical text classification framework: integrating attentive rule construction and neural network |
| title_fullStr | A hybrid medical text classification framework: integrating attentive rule construction and neural network |
| title_full_unstemmed | A hybrid medical text classification framework: integrating attentive rule construction and neural network |
| title_short | A hybrid medical text classification framework: integrating attentive rule construction and neural network |
| title_sort | hybrid medical text classification framework: integrating attentive rule construction and neural network |
| topic | hybrid system; deep learning; attention mechanism; text classification |
| url | https://eprints.nottingham.ac.uk/65138/ https://eprints.nottingham.ac.uk/65138/ https://eprints.nottingham.ac.uk/65138/ |