MIFNet: multimodal interactive fusion network for medication recommendation

Medication recommendation aims to provide clinicians with safe medicine combinations for the treatment of patients. Existing medication recommendation models are built based on the temporal structured code data of electronic health records (EHRs) in the medical database; nevertheless, unstructured data in EHRs, such as textual data containing rich information, are underexploited. To fill the gap, a novel multimodal interactive fusion network (MIFNet) is proposed for medication recommendation, which integrates both structured code information and unstructured text information in EHRs. Our model first extracts a series of informative feature representations to encode comprehensive patient health history and control potential drug-drug interactions (DDI), including medical code, clinical notes, and the DDI knowledge graph. Next, a novel cross-modal interaction extraction block is proposed to capture the intricate interaction information between the two modalities. Finally, a multimodal fusion block is adopted to fuse the constructed features and generate a medication combination list. Experiments are conducted on the public MIMIC-III dataset, and the results demonstrate that the proposed model outperforms the state-of-the-art medication recommendation methods on main evaluation metrics.