Personalized Drug Recommendation System Using Wasserstein Auto-encoders and Adverse Drug Reaction Detection with Weighted Feed Forward Neural Network (WAES-ADR) in Healthcare
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Abstract
In recent years, the use of deep learning approaches in healthcare has yielded promising results in a variety of fields, most notably in the detection of adverse drug reactions (ADRs) and drug recommendations. This paper promises a breakthrough in this field by using Wasserstein autoencoders (WAEs) for personalized medicine recommendation and ADR detection. WAEs' capacity to manage complex data distributions and develop meaningful latent representations makes them ideal for modeling heterogeneous healthcare data. This study intends to improvise the precision and efficiency of drug recommendation systems while also improving patient safety by combining WAEs and early ADR detection strategies. Previous research has used social media data for pharmacovigilance, drug repositioning, and other machine learning algorithms to detect ADRs. However, our proposed methodology offers a novel perspective by combining Wasserstein autoencoders with ADR detection methods, outperforming existing approaches. Preliminary results show that the proposed methodology surpasses current methodologies, with much greater accuracy in ADR identification and medicine recommendation. In particular, the proposed model achieves an ADR detection accuracy of 96.04%, which is 15% higher than the most sophisticated techniques, with considerable improvements in precision, recall, and accuracy metrics. In conclusion, our study seeks to develop customized medicine in healthcare, perhaps leading to dramatically improved patient outcomes and safety.
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