###### Abstract
successfully employed a multi-decoder architecture based on Bidirectional Encoder Representations from Transformers (BERT) to jointly perform tasks such as text similarity assessment, named entity recognition, and textual inference, demonstrating the utility of MTL in handling multiple linguistic objectives simultaneously [14]. Similarly, [PERSON] and [PERSON] applied an MTL framework built on Convolutional Neural Networks (CNNs) to extract relevant features from unstructured clinical notes while concurrently predicting patient mortality, highlighting the advantages of integrating clinical text analysis with outcome prediction [15]. In the domain of medical imaging and computer vision, MTL has also shown considerable promise. [PERSON] et al. utilized a joint learning strategy that combined imaging data with epidemiological variables, demonstrating that auxiliary demographic information can improve image-based predictions [16]. [PERSON] et al. introduced an MTL framework that performs image classification and reconstruction in parallel, thereby reducing the computational burden and enhancing model accuracy through dual-task supervision [17]. Additionally, MTL can be broadly categorized based on the data modality into longitudinal (temporal) and cross-sectional (non-temporal) downstream clinical tasks and applications. For longitudinal applications, [PERSON] et al. proposed a hybrid framework combining Multi-task Gaussian Processes with neural networks to generate real-time, robust risk scores for hospitalized COVID-19 patients [18]. [PERSON] et al. addressed the temporal dimension by treating patient states at different time points as distinct tasks, leveraging recurrent neural networks (RNNs) with attention mechanisms to capture the evolving nature of clinical trajectories [19]. In contrast, cross-sectional applications focus on static data snapshots. For instance, [PERSON] et al. developed an MTL regression model to jointly predict the risk of multiple comorbid conditions, such as congestive heart failure and chronic obstructive pulmonary disease, while also enhancing interpretability by identifying shared disease pathways [20]. [PERSON] et al. conducted a comparative analysis of MTL and single-task learning (STL) frameworks for predicting complications associated with hypertrophic cardiomyopathy, uncovering that MTL consistently outperformed STL in terms of predictive accuracy and model robustness across several classical ML algorithms [21]. While MTL offers an effective strategy to mitigate the issue of limited labeled data in clinical domains, several persistent challenges remain, particularly regarding the quality and consistency of real-world clinical data. One of the most prominent issues in electronic health records (EHRs) is the prevalence of missing data, which can significantly hinder model performance and reliability [22; 23]. Traditional approaches to handling missing values typically involve either deletion of incomplete records or data imputation [24]. However, both strategies have inherent drawbacks; that is, deletion-based methods often lead to the loss of potentially valuable predictive information, reduce dataset size, and may introduce selection bias by systematically excluding specific subpopulations [25]. Conversely, imputation techniques, while preserving data volume, can introduce their own biases, such as distorting the original data distribution or imputing clinically implausible values, thereby degrading the overall data quality. These distortions can be particularly detrimental in clinical applications, where accuracy and interpretability are critical. In addition, traditional single-task DL models often underperform due to limited and imbalanced clinical data, leading high-capacity networks to overfit and exhibit poor generalization. Their single-task structure also hinders the ability to exploit correlations across multiple clinical outcomes.