Mining Biomedical Texts for Pediatric Information

Tian Yun, Deepti Garg, Natalia Khuri

Abstract

To perform a comprehensive and detailed analysis of the gaps in knowledge about drugs’ safety and effectiveness in neonates, infants, children, and adolescents, large collections of complex and unstructured texts need to be analyzed. In this work, machine learning algorithms have been used to implement classifiers of biomedical texts and to extract information about safety and efficacy of drugs in pediatric populations. Models were trained using approved drug product labels and computational experiments were conducted to evaluate the accuracy of the models. A Support Vector Machine with a radial kernel had the best performance by classifying short texts with an accuracy of 94% and an excellent precision. Results show that classifiers perform better when trained using features comprising multiple words rather than single words. The proposed text classifier may be used to mine other sources of biomedical information, such as research publications and electronic health records.

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Paper Citation


in Harvard Style

Yun T., Garg D. and Khuri N. (2021). Mining Biomedical Texts for Pediatric Information.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-490-9, pages 60-71. DOI: 10.5220/0010310200600071


in Bibtex Style

@conference{bioinformatics21,
author={Tian Yun and Deepti Garg and Natalia Khuri},
title={Mining Biomedical Texts for Pediatric Information},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2021},
pages={60-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010310200600071},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Mining Biomedical Texts for Pediatric Information
SN - 978-989-758-490-9
AU - Yun T.
AU - Garg D.
AU - Khuri N.
PY - 2021
SP - 60
EP - 71
DO - 10.5220/0010310200600071