Flat-Lattice-CNN: A model for Chinese medical-named-entity recognition Article Swipe
Shanshan Wang
,
Zhang Kun-yuan
,
Ao Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0331464
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0331464
Background In the field of internet-based healthcare, the complexity of pathology features across various disciplines, coupled with the lack of medical training among most patients, results in medical named entities in doctor patient dialogue texts exhibiting long and multiword syntactic patterns, posing new challenges to named-entity recognition algorithms. Methods To address the issue mentioned above, in this study we integrate Convolutional Neural Networks (CNNs) of different dilation rates on top of Flat-Lattice architecture to construct the Flat-Lattice-CNN model. This model not only considers the semantic information of characters and words, as well as their absolute and relative positional information, but also extracts multiple-token co-occurrence relationship features among characters/words spanning different distances to improve the recognition accuracy of long medical-named entities. Results Experimental results show an improved performance in the task of recognizing medical-named entities on all evaluation datasets, especially on CTDD with a 2.3% increase in F1 score. The proposed Flat-Lattice-CNN model effectively addresses the challenges posed by long and multiword syntactic patterns in medical-named entities, offering improved recognition accuracy and demonstrating the potential for enhancing medical-named-entity recognition in internet-based healthcare dialogues.
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- https://doi.org/10.1371/journal.pone.0331464
- OA Status
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- References
- 22
- OpenAlex ID
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https://doi.org/10.1371/journal.pone.0331464Digital Object Identifier
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Flat-Lattice-CNN: A model for Chinese medical-named-entity recognitionWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-18Full publication date if available
- Authors
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Shanshan Wang, Zhang Kun-yuan, Ao LiuList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0331464Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1371/journal.pone.0331464Direct OA link when available
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0Total citation count in OpenAlex
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22Number of works referenced by this work
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| abstract_inverted_index.medical | 20, 27 |
| abstract_inverted_index.patient | 32 |
| abstract_inverted_index.results | 25, 122 |
| abstract_inverted_index.various | 13 |
| abstract_inverted_index.Networks | 62 |
| abstract_inverted_index.absolute | 94 |
| abstract_inverted_index.accuracy | 115, 169 |
| abstract_inverted_index.dialogue | 33 |
| abstract_inverted_index.dilation | 66 |
| abstract_inverted_index.entities | 29, 133 |
| abstract_inverted_index.extracts | 101 |
| abstract_inverted_index.features | 11, 105 |
| abstract_inverted_index.improved | 125, 167 |
| abstract_inverted_index.increase | 144 |
| abstract_inverted_index.patterns | 162 |
| abstract_inverted_index.proposed | 149 |
| abstract_inverted_index.relative | 96 |
| abstract_inverted_index.semantic | 84 |
| abstract_inverted_index.spanning | 108 |
| abstract_inverted_index.training | 21 |
| abstract_inverted_index.addresses | 153 |
| abstract_inverted_index.considers | 82 |
| abstract_inverted_index.construct | 74 |
| abstract_inverted_index.datasets, | 137 |
| abstract_inverted_index.distances | 110 |
| abstract_inverted_index.enhancing | 175 |
| abstract_inverted_index.entities, | 165 |
| abstract_inverted_index.entities. | 119 |
| abstract_inverted_index.integrate | 59 |
| abstract_inverted_index.mentioned | 53 |
| abstract_inverted_index.multiword | 38, 160 |
| abstract_inverted_index.offering | 166 |
| abstract_inverted_index.pathology | 10 |
| abstract_inverted_index.patients, | 24 |
| abstract_inverted_index.patterns, | 40 |
| abstract_inverted_index.potential | 173 |
| abstract_inverted_index.syntactic | 39, 161 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.challenges | 43, 155 |
| abstract_inverted_index.characters | 87 |
| abstract_inverted_index.complexity | 8 |
| abstract_inverted_index.dialogues. | 181 |
| abstract_inverted_index.different | 65, 109 |
| abstract_inverted_index.especially | 138 |
| abstract_inverted_index.evaluation | 136 |
| abstract_inverted_index.exhibiting | 35 |
| abstract_inverted_index.healthcare | 180 |
| abstract_inverted_index.positional | 97 |
| abstract_inverted_index.algorithms. | 47 |
| abstract_inverted_index.healthcare, | 6 |
| abstract_inverted_index.information | 85 |
| abstract_inverted_index.performance | 126 |
| abstract_inverted_index.recognition | 46, 114, 168, 177 |
| abstract_inverted_index.recognizing | 131 |
| abstract_inverted_index.Experimental | 121 |
| abstract_inverted_index.Flat-Lattice | 71 |
| abstract_inverted_index.architecture | 72 |
| abstract_inverted_index.disciplines, | 14 |
| abstract_inverted_index.effectively | 152 |
| abstract_inverted_index.information, | 98 |
| abstract_inverted_index.named-entity | 45 |
| abstract_inverted_index.relationship | 104 |
| abstract_inverted_index.Convolutional | 60 |
| abstract_inverted_index.co-occurrence | 103 |
| abstract_inverted_index.demonstrating | 171 |
| abstract_inverted_index.medical-named | 118, 132, 164 |
| abstract_inverted_index.internet-based | 5, 179 |
| abstract_inverted_index.multiple-token | 102 |
| abstract_inverted_index.Flat-Lattice-CNN | 76, 150 |
| abstract_inverted_index.characters/words | 107 |
| abstract_inverted_index.medical-named-entity | 176 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5076376921, https://openalex.org/A5107923313, https://openalex.org/A5007984042 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I2722730, https://openalex.org/I4210102412 |
| citation_normalized_percentile.value | 0.1948643 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |