ML-MedScan: A Transformer-Based Architecture for Early Disease  Prediction from Multimodal Medical Data

Shashi Raj,Reena Kumari ,Rashmi Rani

ML-MedScan: A Transformer-Based Architecture for Early Disease Prediction from Multimodal Medical Data

Authors

Shashi Raj,Reena Kumari ,Rashmi Rani

Keywords:

transformer architecture, early disease prediction, multimodal medical data, deep learning, personalized medicine, healthcare analytics

Abstract

Early disease prediction remains a critical challenge in healthcare, with traditional diagnosticapproaches often detecting conditions only after significant progression. This study introducesML-MedScan, a novel transformer-based architecture designed to predict disease

References

Brown, S. A., Wilson, K. M., & Davis, R. T. (2023). Temporal modeling in healthcare: Challenges and opportunities. Nature Medicine, 29(4), 845-862. https://doi.org/10.1038/s41591-023-02341-x

Chen, L., Zhang, Y., & Liu, W. (2023). Artificial intelligence in preventive medicine: Current applications and future directions. The Lancet Digital Health, 5(8), e512-e524. https://doi.org/10.1016/S2589-7500(23)00089-7

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Published

2024-09-21

How to Cite

Shashi Raj,Reena Kumari ,Rashmi Rani. (2024). ML-MedScan: A Transformer-Based Architecture for Early Disease Prediction from Multimodal Medical Data. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 5143–5161. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3159