Disease prediction using NLP techniques

\nThis 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.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-12-13
DOI
https://doi.org/10.1051/itmconf/20246903001/pdf
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Disease prediction using NLP techniques

Springer Link (Chiba Institute of Technology)
Machine Learning in Healthcare
article

Disease prediction using NLP techniques

article en

Abstract

\nThis 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.\n

Springer Link (Chiba Institute of Technology)
Quality Education
Openalex Percentile: Top 64%
Machine Learning in Healthcare
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Disease prediction using NLP techniques · Springer Link (Chiba Institute of Technology) (2024) | TGRS Research Map | TGRS