DHAN: A deep hierarchical attention network for multisource biomedical data fusion in precision diabetic retinopathy diagnosis

Objectives To establish a robust and clinically applicable approach for integrating heterogeneous multisource biomedical data, particularly continuous glucose monitoring (CGM) profiles and structured electronic health records (EHRs), in order to enhance the diagnostic accuracy and clinical utility of diabetic retinopathy (DR) detection. Methods This study proposed a deep hierarchical attention network (DHAN) for multisource biomedical data fusion. First, to address the heterogeneous forms of different data sources, two specific subencoders were designed, a hybrid architecture for time-series CGM sensors and a structured encoder for EHRs. Second, an entity-embedding mechanism was added to the EHR subencoder to fuse heterogeneous feature types within EHRs. Finally, a deep hierarchical attention mechanism was proposed to dynamically capture inner-source saliency and inter-source correlations. Results Using the dataset provided by Shanghai Sixth People's Hospital, 559 patients were included, comprising 157 with DR and 402 without. DHAN achieved the best performance across multiple experiments, with a diagnostic accuracy of 0.89. Its comprehensive performance, including an F 1-score of 0.80 and a G -mean of 0.89, further demonstrates its robustness. Conclusions The results indicate that DHAN is a viable approach for diagnosing DR in patients with type 2 diabetes. By effectively fusing multisource heterogeneous data, DHAN can be embedded within CGM sensors to enable remote concurrent diagnosis of DR. Moreover, it provides a generalizable paradigm for multisensor systems requiring fusion of data from multiple sources.