###### Abstract  
Accurately predicting patient clinical outcomes is a complex task that requires integrating diverse factors, including individual characteristics, treatment histories, and environmental influences. This challenge is further exacerbated by missing data and inconsistent data quality, which often hinder the effectiveness of traditional single-task learning (STL) models. Multi-Task Learning (MTL) has emerged as a promising paradigm to address these limitations by jointly modeling related prediction tasks and leveraging shared information. In this study, we proposed MTLNFM, a multi-task learning framework built upon Neural Factorization Machines, to jointly predict patient clinical outcomes on a cohort of 2001 ICU patients. We designed a preprocessing strategy in the framework that transforms missing values into informative representations, mitigating the impact of sparsity and noise in clinical data. We leveraged the shared representation layers, composed of a factorization machine and dense neural layers that can capture high-order feature interactions and facilitate knowledge sharing across tasks for the prediction. We conducted extensive comparative experiments, demonstrating that MTLNFM outperforms STL baselines across all three tasks (i.e., frailty status, hospital length of stay and mortality prediction), achieving AUROC scores of 0.7514, 0.6722, and 0.7754, respectively. A detailed case analysis further revealed that MTLNFM effectively integrates both task-specific and shared representations, resulting in more robust and realistic predictions aligned with actual patient outcome distributions. Overall, our findings suggest that MTLNFM is a promising and practical solution for clinical outcome prediction, particularly in settings with limited or incomplete data, and can support more informed clinical decision-making and resource planning. multi-task learning; neural factorization machine; machine learning; clinical outcome prediction 2020 IEEE/RSJ International Conference on Intelligent Intelligent Systems (IEEE), Vol., pp. 1-10, 2018-2020, 20  
care, and optimizing the allocation of limited healthcare resources [1]. With the increasing availability of multi-modal clinical data including electronic health records (EHRs), predictive modeling has emerged as a powerful tool to extract meaningful insights and support clinical decision support systems (CDSS) [2; 3; 4]. Among various machine learning (ML) approaches, deep learning (DL) has demonstrated remarkable success in numerous biomedical tasks due to its capacity to learn hierarchical representations from large and complex datasets [5; 6; 7; 8]. However, the performance of deep learning models is highly dependent on the availability of large-scale labeled data, which poses a significant challenge in clinical domains, where annotations are often costly, labor-intensive, and require expert validation [5; 9; 10]. To address these limitations, Multi-Task Learning (MTL) has gained attention as a promising strategy to improve learning efficiency and generalization by simultaneously training a model on multiple related tasks [5; 11], which exploits the underlying commonalities among tasks, enabling shared representation learning and reducing the risk of overfitting in scenarios with limited data. From a cognitive perspective, MTL emulates how the human brain leverages prior knowledge across domains to facilitate learning, making it particularly suited for complex and interrelated clinical prediction problems [5; 11; 12; 13]. By promoting information sharing across tasks, MTL can enhance model robustness, reduce task-specific biases, and improve overall performance in both primary and auxiliary outcomes. In recent years, MTL has gained significant traction in the biomedical and clinical domains due to its ability to leverage shared knowledge across tasks, thereby enhancing model generalizability and performance. For example, [PERSON] et al.