Disease prediction using NLP techniques
This paper explores the application of the T5 (Text-To-Text Transfer Transformer) model Originating from the groundbreaking “Attention Is All You Need” concept, fine-tuned on a medical dataset to predict diseases and symptoms from unstructured medical reports. By leveraging Natural Language Processi...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_03001.pdf |
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| _version_ | 1841554721848950784 |
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| author | Hamza Ouabiba Farah Sniba |
| author_facet | Hamza Ouabiba Farah Sniba |
| author_sort | Hamza Ouabiba |
| collection | DOAJ |
| description | This paper explores the application of the T5 (Text-To-Text Transfer Transformer) model Originating from the groundbreaking “Attention Is All You Need” concept, fine-tuned on a medical dataset to predict diseases and symptoms from unstructured medical reports. By leveraging Natural Language Processing (NLP), the system offers automated analysis, enabling quicker and more accurate diagnoses based on symptoms provided by users. The fine- tuning process involved training the T5 model to adapt to the specific language and context of medical texts. The model’s performance is evaluated based on its ability to detect and predict medical conditions from user inputs. |
| format | Article |
| id | doaj-art-9fb276aa5dda4c29ae88b78f674bfa11 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-9fb276aa5dda4c29ae88b78f674bfa112025-01-08T10:58:54ZengEDP SciencesITM Web of Conferences2271-20972024-01-01690300110.1051/itmconf/20246903001itmconf_maih2024_03001Disease prediction using NLP techniquesHamza Ouabiba0Farah Sniba1LAMIGEP/EMSI-MARRAKECHLAMIGEP/EMSI-MARRAKECHThis paper explores the application of the T5 (Text-To-Text Transfer Transformer) model Originating from the groundbreaking “Attention Is All You Need” concept, fine-tuned on a medical dataset to predict diseases and symptoms from unstructured medical reports. By leveraging Natural Language Processing (NLP), the system offers automated analysis, enabling quicker and more accurate diagnoses based on symptoms provided by users. The fine- tuning process involved training the T5 model to adapt to the specific language and context of medical texts. The model’s performance is evaluated based on its ability to detect and predict medical conditions from user inputs.https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_03001.pdf |
| spellingShingle | Hamza Ouabiba Farah Sniba Disease prediction using NLP techniques ITM Web of Conferences |
| title | Disease prediction using NLP techniques |
| title_full | Disease prediction using NLP techniques |
| title_fullStr | Disease prediction using NLP techniques |
| title_full_unstemmed | Disease prediction using NLP techniques |
| title_short | Disease prediction using NLP techniques |
| title_sort | disease prediction using nlp techniques |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_03001.pdf |
| work_keys_str_mv | AT hamzaouabiba diseasepredictionusingnlptechniques AT farahsniba diseasepredictionusingnlptechniques |