Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing
A modern framework for assessing patient histories and conducting clinical research has been developed as the number of clinical narratives evolves. To discover the knowledge from such clinical narratives, clinical entity recognition and relation extraction tasks were performed subsequently in exist...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-12-01
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| Series: | Journal of the Scientific Society |
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| Online Access: | https://journals.lww.com/10.4103/jss.jss_73_23 |
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| author | Naveen S. Pagad Pradeep Nijalingappa Tulika Chakrabarti Prasun Chakrabarti Pugazhenthan Thangaraju |
| author_facet | Naveen S. Pagad Pradeep Nijalingappa Tulika Chakrabarti Prasun Chakrabarti Pugazhenthan Thangaraju |
| author_sort | Naveen S. Pagad |
| collection | DOAJ |
| description | A modern framework for assessing patient histories and conducting clinical research has been developed as the number of clinical narratives evolves. To discover the knowledge from such clinical narratives, clinical entity recognition and relation extraction tasks were performed subsequently in existing approaches, which resulted in error propagation. Therefore, a novel end-to-end clinical knowledge discovery strategy has been proposed in this paper. The clinical XLNet was used as a base model for handling the discrepancy issue. To predict the dependent clinical relation association, the multinomial Naïve Bayes probability function has been incorporated. In order to improve the performance of the proposed strategy, it takes into account entity pairs presented consecutively through the multi-head attention layer. Tests have been conducted using the N2C2 corpus, and the proposed methodology achieves a greater than 20% improvement in accuracy over existing neural network-based and transformer-based methods. |
| format | Article |
| id | doaj-art-bd79a71447d14458af3f36402ae94c11 |
| institution | Kabale University |
| issn | 0974-5009 2278-7127 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of the Scientific Society |
| spelling | doaj-art-bd79a71447d14458af3f36402ae94c112025-01-07T07:23:01ZengWolters Kluwer Medknow PublicationsJournal of the Scientific Society0974-50092278-71272024-12-0151451152110.4103/jss.jss_73_23Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language ProcessingNaveen S. PagadPradeep NijalingappaTulika ChakrabartiPrasun ChakrabartiPugazhenthan ThangarajuA modern framework for assessing patient histories and conducting clinical research has been developed as the number of clinical narratives evolves. To discover the knowledge from such clinical narratives, clinical entity recognition and relation extraction tasks were performed subsequently in existing approaches, which resulted in error propagation. Therefore, a novel end-to-end clinical knowledge discovery strategy has been proposed in this paper. The clinical XLNet was used as a base model for handling the discrepancy issue. To predict the dependent clinical relation association, the multinomial Naïve Bayes probability function has been incorporated. In order to improve the performance of the proposed strategy, it takes into account entity pairs presented consecutively through the multi-head attention layer. Tests have been conducted using the N2C2 corpus, and the proposed methodology achieves a greater than 20% improvement in accuracy over existing neural network-based and transformer-based methods.https://journals.lww.com/10.4103/jss.jss_73_23clinical xlnetelectronic health recordentity recognitionknowledge discoverynatural language processingrelation extraction |
| spellingShingle | Naveen S. Pagad Pradeep Nijalingappa Tulika Chakrabarti Prasun Chakrabarti Pugazhenthan Thangaraju Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing Journal of the Scientific Society clinical xlnet electronic health record entity recognition knowledge discovery natural language processing relation extraction |
| title | Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing |
| title_full | Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing |
| title_fullStr | Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing |
| title_full_unstemmed | Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing |
| title_short | Clinical XLNet-based End-to-End Knowledge Discovery on Clinical Text Data using Natural Language Processing |
| title_sort | clinical xlnet based end to end knowledge discovery on clinical text data using natural language processing |
| topic | clinical xlnet electronic health record entity recognition knowledge discovery natural language processing relation extraction |
| url | https://journals.lww.com/10.4103/jss.jss_73_23 |
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