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Deep learning-based image classification for predicting medical outcomes in road traffic crashes

artificial-intelligence
brain-computer-interface
computational-linguistics
data-mining-and-machine-learning

Abstract

Background: Road traffic collisions (RTCs) continue to pose a critical global public health challenge, necessitating innovative and interdisciplinary approaches to mitigate their impact—particularly in terms of medical outcomes. Traditional predictive models in RTC contexts often rely on conventional machine learning and statistical techniques, which struggle with limited generalization across diverse clinical scenarios, poor integration of heterogeneous data modalities, and low interpretability for medical decision-making.

Objective: This study introduces a novel deep learning-based framework that leverages Clinical Knowledge Transformer (CKT) and Domain-Aware Adaptive Reasoning (DAAR) methodologies to enhance the prediction of medical outcomes following RTCs.

Methods: The proposed approach systematically integrates multimodal patient data—including diagnostic imaging and clinical records—through advanced semantic fusion and probabilistic reasoning over clinical knowledge graphs. This promotes transparent and generalizable inference processes. The CKT architecture extracts latent clinical concepts while enforcing symbolic constraints derived from clinical guidelines, ensuring clinically valid decision pathways. The DAAR strategy enables dynamic domain adaptation, which is essential for maintaining predictive robustness across varying clinical environments and data heterogeneities common in road traffic injury scenarios.

Results: Experimental evaluations demonstrate that the proposed method significantly outperforms traditional deep learning baselines and conventional symbolic models in predictive accuracy, interpretability, and cross-domain adaptability.

Conclusion: This work aligns with emerging multidisciplinary efforts to integrate advanced technological solutions into RTC mitigation strategies. It addresses key themes of knowledge representation, model interpretability, and clinical domain generalization—critical for improving healthcare responses to traffic-related injuries.

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