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Synergistic Fusion of Deep Learning and Knowledge Graphs for Enhanced Clinical Diagnosis and Personalized Treatment Prediction

Authors: Anirudh Pratap Singh

Published: 2025-05-02 01:08:31.238

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Abstract

This paper explores the synergistic integration of deep learning techniques and knowledge graphs for enhancing clinical diagnosis and personalized treatment prediction. We address the limitations of traditional clinical decision support systems by leveraging the power of deep learning to extract intricate patterns from heterogeneous clinical data, while simultaneously utilizing knowledge graphs to represent and reason over complex biomedical relationships. We propose a novel framework that combines Graph Neural Networks (GNNs) with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture both structured and unstructured data representations. The framework is evaluated on a large-scale clinical dataset, demonstrating significant improvements in diagnostic accuracy and treatment outcome prediction compared to state-of-the-art methods. Furthermore, we investigate the interpretability of the proposed model, providing insights into the key factors influencing diagnostic and treatment decisions. The results highlight the potential of this integrated approach to revolutionize healthcare by providing clinicians with more accurate, personalized, and explainable decision support tools.

Keywords

Deep Learning, Knowledge Graphs, Clinical Diagnosis, Personalized Treatment, Artificial Intelligence, Healthcare, Graph Neural Networks, Natural Language Processing, Medical Informatics, Predictive Modeling

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How to Cite

Anirudh Pratap Singh, (2025-05-02 01:08:31.238). Synergistic Fusion of Deep Learning and Knowledge Graphs for Enhanced Clinical Diagnosis and Personalized Treatment Prediction. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 3.

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