## 3 Experiment and Discussion
### Results and Analysis
Notably, the performance of the model in this paper shows differentiated characteristics in multiple tasks. The performance improvement in mortality prediction is mainly reflected in the accurate recognition of minority samples (AUPRC increased by 4.7%), while in the prediction of hospital stay, it significantly surpassed the comparison model by integrating cross-modal features. From the experimental results, we can analyze that the CNN-based models, MTL, MDCNN, and CMN, rely on CNN to extract clinical features, but are constrained by the long-term dependency problem, making it difficult for the model to learn the contextual relationship between long sentences, resulting in limitations in the model's prediction performance. Moreover, the attention-based models, BCB + LSTM, PM\({}^{2}\)P\({}^{2}\)N, and DESAM-cp, all achieved good performance in the same test set. We speculate that this is because the attention-based models all use the attention mechanism to extract feature information, allowing the model to better learn the interdependence between features. This also proves the rationality of using the cross-modal attention mechanism when designing the MCA model in this paper, which helps the model further optimize the cross-modal interaction of clinical notes and time series data. Although the attention-based model can achieve  
Figure 5: Radar chart of model performance metrics. better performance through feature dependency learning, the BCB + LSTM model lacks full consideration of the dependency between sentences, and limits the model's ability to capture key information by simply averaging sentence embeddings. We use the BiGRU network to process time series data. BiGRU processes time series data in both forward and backward directions, and can capture past and future information at the same time. This bidirectional information flow enables it to more comprehensively understand the dependencies in the time series, thereby providing more accurate feature representation in clinical outcome prediction. LSTM mainly processes data in the forward direction. Although it can capture long-term dependencies, it may miss some key features due to the lack of backward information flow. In medical data, the health status of patients is not only affected by past medical history, but also related to future examination and treatment trends. Therefore, this feature of BiGRU is particularly important for capturing changes in the health status of patients. The experimental results show that BiGRU has achieved excellent performance in multiple clinical outcome prediction tasks, which further verifies its theoretical advantages. However, BiGRU requires more computing resources and training time, and may face the problem of gradient vanishing or gradient exploding when processing very long sequences. In future work, strategies such as gradient clipping and optimization can be considered to alleviate these problems. From the perspective of feature learning, PM\({}^{2}\)F\({}^{2}\)N and CMN extract key medical features and combine them with time series data, improving the performance of the model to some extent, but there are certain limitations. Firstly, such models only focus on discrete medical entities and fail to fully learn the fine-grained interactions between cross-modal data. Secondly, due to the large number of entity categories in clinical notes, the training process is prone to over-reliance on specific entity types. Finally, the model ignores the structural associations and temporal dependencies between entities, which ultimately limits the prediction performance of such models. Based on the above analysis, this paper proposes a dual-granularity attention mechanism, which captures key medical concepts through entity-level features from a local perspective and models global semantic relationships using sentence-level features from a global perspective.