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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. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening medical condition that is characterized by the dysregulated immune system response to infections, having both high morbidity and mortality rates. Early prediction of sepsis is critical to the decrease of mortality. This paper presents a novel early warning model called Double Fusion Sepsis Predictor (DFSP) for sepsis onset. DFSP is a double fusion framework that combines the benefits of early and late fusion strategies. First, a hybrid deep learning model that combines both the convolutional and recurrent neural networks to extract deep features is proposed. Second, deep features and handcrafted features, such as clinical scores, are concatenated to build the joint feature representation (early fusion). Third, several tree-based models based on joint feature representation are developed to generate the risk scores of sepsis onset that are combined with an End-to-End neural network for final sepsis detection (late fusion). To evaluate DFSP, a retrospective study was conducted, which included patients admitted to the ICUs of a hospital in Shanghai China. The results demonstrate that the DFSP outperforms state-of-the-art approaches in early sepsis prediction. [ABSTRACT FROM AUTHOR]