Document is current

Article History

Received: 24 January 2025

Accepted: 4 May 2025

First Online: 5 June 2025

Declarations

:

: The authors declare no competing interests.

: While the MS-TimeXer-MoE model demonstrates superior predictive performance for traumatic hemorrhagic shock, its practical implementation presents several challenges that must be carefully considered for successful clinical deployment. The model’s computational demands are substantial due to its sophisticated architecture, which incorporates multi-scale embeddings, attention mechanisms, and a mixture-of-experts framework. In our study, training was conducted on a 24 GB NVIDIA GeForce RTX 3090 GPU over 100 epochs with a learning rate of 0.0001, highlighting the need for high-performance hardware. This resource intensity could limit real-time application in resource-constrained settings, such as rural or underfunded healthcare facilities, where rapid predictions are essential for timely intervention. Additionally, the model’s reliance on large, high-quality time-series datasets poses a significant challenge. Our use of the MIMIC IV database, which provides comprehensive and clean data, underscores the model’s dependency on consistent inputs. However, real-world clinical data are often plagued by irregular sampling, missing values, and noise, which could degrade the model’s performance. Training on a single-center dataset further raises concerns about generalizability across diverse patient populations and healthcare systems. Moreover, the interpretability of the MS-TimeXer-MoE model is inherently limited by its complexity. The multi-expert design and attention mechanisms, while powerful, create a"black-box"effect, making it difficult for clinicians to understand how specific inputs—such as a sudden change in heart rate—influence predictions. This lack of transparency stands in contrast to simpler models like Ridge Regression, which offers clear coefficients, and may reduce clinician trust in the model’s outputs, hindering its adoption. Addressing these challenges will require concerted efforts in algorithmic optimization, robust data handling, and the development of interpretability tools, such as attention visualizations, to ensure that the MS-TimeXer-MoE can be effectively translated into a practical clinical tool.