Breaking the Language Barrier in Medical Research: Extracting Disease Features and Translating to Any Language with NLP and NLTK

DOI

10.2991/978-94-6463-294-1_9How to use a DOI?

Keywords

Natural Language Processing; Healthcare; Python; Disease; Medical Literature; Languages

Abstract

Language barriers can hinder the progress of medical research, particularly in global health where access to information in multiple languages is critical. Natural Language Processing (NLP) and the Natural Language Toolkit (NLTK) can be used to extract disease features from medical literature and translate them to any language. Medical research is a field that requires extensive collaboration and communication among researchers from diverse backgrounds and locations. One major challenge in this field is the language barrier, where research findings and medical terminology are often expressed in different languages, hindering effective knowledge sharing and collaboration. Natural Language Processing (NLP) and Natural Language Toolkit (NLTK) are technologies that can help break down this barrier by enabling automated extraction and translation of key disease features from various sources. The study used a Python-based NLP algorithm to extract disease features and medicinal plant information from medical literature across multiple languages, including Hindi, Telugu and other Indian languages. The process involves collecting medical texts in multiple languages, preprocessing the data using NLP techniques, extracting disease features using NLTK tools, translating the extracted features to any language, analyzing and comparing the disease features across languages. The NLP was able to identify disease features and medicinal plants with high accuracy, and the machine translation component was able to translate the extracted information to any language with reasonable accuracy. The pipeline was able to break language barriers in medical research by providing access to information in multiple languages. The NLP and NLTK can be effective tools for breaking language barriers in medical research. This approach can be used to provide access to medical information in multiple languages, enabling researchers to collaborate and share knowledge across borders and languages. Overall, this study highlights the potential of NLP and NLTK in breaking language barriers in medical research and improving global health outcomes.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rehan Khan
AU  - Preenon Bagchi
PY  - 2023
DA  - 2023/11/17
TI  - Breaking the Language Barrier in Medical Research: Extracting Disease Features and Translating to Any Language with NLP and NLTK
BT  - Proceedings of the International Conference on Advances in Nano-Neuro-Bio-Quantum (ICAN 2023)
PB  - Atlantis Press
SP  - 102
EP  - 109
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-294-1_9
DO  - 10.2991/978-94-6463-294-1_9
ID  - Khan2023
ER  -