A hybrid mamba-transformer architecture fusing clinical and genetic features for gestational diabetes mellitus prediction.

Authors

Ji Huang, Wenbing Shi, Lan Lin, Zhongliang Wei, Qianwen Li

Published in

PloS one. Volume 21. Issue 8. Pages e0355729. Epub Aug 12, 2026.

Abstract

Gestational diabetes mellitus (GDM) is a common disorder of glucose metabolism during pregnancy. Early GDM prediction is crucial for reducing adverse maternal and neonatal outcomes. This paper proposes a hybrid Mamba-Transformer architecture that aggregates clinical and genetic features for GDM prediction. First, a correlation-driven weighted fusion method for clinical and genetic features is introduced. The integrated representation not only enhances feature representation but also highlights the interactive relationship between genetic susceptibility and clinical factors. Second, a sliding window approach is applied to reconstruct the sample sequences from the preprocessed data, generating augmented instances as model input. This transforms isolated individual features into context-aware group features, enabling the effective capture of both population-level heterogeneity and individual risk. Finally, the hybrid Mamba-Transformer architecture is constructed and trained on the publicly available competition dataset (DMRPD) from the Alibaba Cloud Tianchi platform. The model employs a modular and extensible encoder-decoder structure, where the Mamba module serves as an efficient feature extractor for dependencies, while the Transformer module performs deep semantic modeling and sequence abstraction. Experimental results indicate that the proposed method achieves competitive performance compared with other representative models. Specifically, the model attained an AUC of 0.825 on the test set, with sensitivity and specificity at the optimal threshold (0.526) of 0.827 and 0.729, respectively, suggesting reliable discriminative performance on the test set. These findings suggest that the proposed method may provide a useful approach for early GDM risk prediction and could potentially support more targeted screening strategies. However, further validation using larger and independent cohorts is required to confirm its generalizability and clinical applicability.

PMID:
42585183
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.

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