## 4 Discussion  
Multi-Task Learning (MTL) has emerged as a powerful strategy for addressing the pervasive challenge of data scarcity in biomedical and clinical applications [5; 11]. By enabling the simultaneous learning of multiple related tasks, MTL facilitates shared representation learning that leverages commonalities across domains, leading to improved generalization, model robustness, and sample efficiency, particularly in settings where labeled data is sparse or imbalanced. This makes MTL particularly valuable in healthcare, where data annotation is often costly, labor-intensive, and subject to domain-specific expertise constraints. A growing body of research has demonstrated the utility of MTL in various healthcare contexts [41; 42; 43; 44], including clinical natural language processing [45; 46], medical imaging [47; 48; 49] and disease risk prediction [50; 51]. These applications illustrate MTL's potential to exploit inter-task dependencies for enhanced performance and interpretability. Despite these advancements, the effectiveness of MTL models can be significantly undermined by the inherent limitations in clinical data quality, especially the high prevalence of missing, incomplete, or noisy values. Missing data is a well-recognized challenge in EHRs due to irregular sampling, variability in clinical workflows, and incomplete documentation [52]. Common data preprocessing techniques, such as deletion of incomplete records or imputation of missing values, can inadvertently compromise data integrity, introduce selection bias, and distort the underlying data distribution. These issues, in turn, may negatively affect model performance, reduce reliability, and hinder generalizability in real-world clinical settings. Therefore, developing MTL frameworks that are robust to data imperfections,  
Figure 4: Total deviation of STLNFM and MTLNFM models. capable of leveraging patterns of missingness, and adaptive to heterogeneous healthcare data modalities (e.g., structured data, free-text notes, and images) remains a critical and open area of research. Recent efforts have explored incorporating learned embeddings for missingness patterns, uncertainty-aware modeling, and causal inference techniques to better align predictive models with the complexities of real-world clinical data. In this study, we introduced a novel MTL framework named MTLNFM for multi-task clinical outcome prediction concurrently using neural factorization machines. This framework not only addresses the issues associated with missing data through an innovative labeling strategy but also effectively integrates multiple predictive tasks, thereby improving overall performance. The experimental results demonstrate the superiority of the MTLNFM model over the STL models (i.e., STLNFM) across various evaluation metrics. Our findings show that MTLNFM achieved superior performance in predicting hospital length of stay, both in the 2-task and 3-task configurations, supporting our hypothesis that MTL can exploit task-relatedness to capture more comprehensive and informative patterns. Notably, we observed that multi-task learning involving Clinical Frailty Scale (CFS) and hospital mortality yielded better results than the combination of length of stay and hospital mortality, likely due to a stronger intrinsic correlation between frailty and mortality outcomes. To further validate the model's effectiveness, we conducted a case study on an independent testing set. The analysis revealed that MTLNFM's predicted feature distributions, e.g., CCI, creatinine, and urea, are closely aligned with the ground truth, especially for patients with shorter hospital stays. In contrast, STLNFM exhibited significant prediction deviations, particularly for patients with chronic conditions like CKD, obesity, dementia, and cancer, often overestimating their length of stay. This highlights MTLNFM's ability to accommodate a broader range of patient profiles, reflecting its robustness, generalizability, and potential applicability in real-world clinical decision-making.