An AI-enabled research support tool for the classification system of COVID-19

Frontiers in Public Health

Public Health

Shilpa Srivastava¹  Arti Tiwari²  Millie Pant³  Vaclav Snasel⁴  Kamanasish Bhattacharjee⁵ 

[1] CHRIST (Deemed to be University) Delhi NCR, Ghaziabad, India;Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India;Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India;Mehta Family School for Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India;Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czechia;Machine Intelligence in Medicine and Imaging (MI-2) Lab, Mayo Clinic, Phoenix, AZ, United States;

关键词: COVID-19;    long short-term memory;    classification;    bi-directional LSTM;    Artificial Intelligence;   

DOI  :  10.3389/fpubh.2023.1124998

 received in 2022-12-15, accepted in 2023-02-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM).

【 授权许可】

Unknown   
Copyright © 2023 Tiwari, Bhattacharjee, Pant, Srivastava and Snasel.

【 图 表 】

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