Incorporating additional data modalities, such as biomarkers or patient history, into the multimodal fusion approach can significantly enhance the predictive performance of the model. One way to extend the approach is by integrating biomarker data, which can provide valuable insights into the physiological state of the patient. Biomarkers related to stroke, such as levels of specific proteins or genetic markers, can be included as input features. These biomarkers can be processed using appropriate feature extraction techniques and then fused with existing image and text data through the multimodal fusion framework.
Patient history is another crucial data modality that can be integrated into the model. Information about previous medical conditions, medications, lifestyle factors, and demographic details can offer a comprehensive view of the patient's health status. By incorporating patient history data, the model can capture long-term trends and patterns that may influence stroke outcomes. This data can be encoded into suitable representations and combined with existing modalities using the fusion mechanism.
To implement this extension effectively, it is essential to preprocess the additional data modalities appropriately, ensuring compatibility with the existing input formats. Feature engineering techniques can be employed to extract relevant information and create meaningful representations for fusion. Furthermore, the fusion module may need to be adapted to handle the diverse nature of the new data modalities and facilitate effective integration with the existing modalities. By incorporating biomarkers and patient history into the multimodal fusion framework, the model can leverage a more comprehensive set of information for improved stroke outcome prediction.