Predicting Drug Side-effects from Open Source Health Forums using Supervised Classifier Approach-Reference-Cited by-同舟云学术

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Predicting Drug Side-effects from Open Source Health Forums using Supervised Classifier Approach

Published:2020-06 Issue: Volume: Page:
ISSN:
Container-title:2020 5th International Conference on Communication and Electronics Systems (ICCES)
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Author:

Swathi D. Naga,U. Kumaran.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9130794/9137848/09138065.pdf?arnumber=9138065

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Drug Satisfaction Analytics: Investigating the Effectiveness of Various Embedding Techniques;2024 IEEE 9th International Conference for Convergence in Technology (I2CT);2024-04-05

2. Inceptionv3‐LSTM‐COV: A multi‐label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short‐term memory;ETRI Journal;2024-02-25

3. Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images;IEEE Access;2024

4. Drug Prediction Based on Customer Reviews;2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT);2023-11-23

5. MLCNN-CDSE: A Multi-Label Convolutional Neural Network Model for Predicting COVID Drug Side Effects from Images of Stick Structure-Based Chemical Conformers;2023 IEEE Silchar Subsection Conference (SILCON);2023-11-03

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