Individuals with severe mental illness (SMI) face a 10-20 year shorter life expectancy compared to the general population, with most premature deaths attributed to multimorbidity—co-existing chronic conditions. Despite research on disease prevalence and clusters, little is known about how multimorbidities interact with treatments and impact long-term health outcomes for SMI patients. This knowledge gap is largely due to the lack of large-scale, longitudinal studies examining these interactions over time.  This project aims to bridge this gap by analyzing extensive electronic health records (EHRs) from the South London and Maudsley NHS Foundation Trust (SLaM), which serves 1.3 million residents. The research will proceed in three key stages:  Data Linkage: Using the Clinical Record Interactive Search (CRIS) system, we will link SLaM’s mental health data with Hospital Episode Statistics (HES) and primary care data from Lambeth DataNet, creating a comprehensive longitudinal dataset for individuals with SMI. Interaction Analysis: We will apply multi-layer network analysis to examine interactions between diagnoses and treatments over time, using advanced techniques like Graph Transformers to predict risks and service utilization. Dynamics Analysis: We will study disease trajectories and their evolution, exploring reinforcement learning and language models to understand how interventions and risk factors influence disease progression. This project will enhance our understanding of multimorbidity dynamics in SMI and inform more effective care strategies to reduce mortality in this population.   6.Quality Control To ensure the integrity, accuracy, and reliability of the project's outcomes, a comprehensive quality control (QC) framework will be implemented at every stage of the research process. Key elements of this framework include: Data Validation and Preprocessing: Prior to model development, the data sourced from the CRIS system and its linked datasets will undergo rigorous validation and preprocessing. This will involve correcting errors, addressing missing data, and ensuring consistency across both structured and unstructured data sources. Model Testing and Evaluation: Continuous evaluation will be conducted throughout the model development process to assess performance, accuracy, and robustness. Key metrics such as precision, recall, F1-score, and area under the curve (AUC) will be used to measure and compare model performance. Cross-Validation and Overfitting Mitigation: To prevent overfitting and enhance the model's generalizability, cross-validation techniques will be applied. The model will be trained on various subsets of the data and tested on unseen portions to ensure its reliability and performance in diverse scenarios. Inter-rater Reliability: For tasks involving manual annotations, such as reviewing clinical records, inter-rater reliability will be monitored to ensure consistency between different reviewers. Any discrepancies will be addressed through calibration processes and consensus meetings. References  [1] Hayes, Joseph F., Louise Marston, Kate Walters, Michael B. King, and David P. J. Osborn. 2017. “Mortality Gap for People with Bipolar Disorder and Schizophrenia: UK-Based Cohort Study 2000-2014.” The British Journal of Psychiatry: The Journal of Mental Science 211 (3): 175–81.  [3] Wang, Tao, Rebecca Bendayan, Yamiko Msosa, Megan Pritchard, Angus Roberts, Robert Stewart, and Richard Dobson. 2022. “Patient-Centric Characterization of Multimorbidity Trajectories in Patients with Severe Mental Illnesses: A Temporal Bipartite Network Modeling Approach.” Journal of Biomedical Informatics 127 (March): 104010. [3] Shehzad, A., Xia, F., Abid, S., Peng, C., Yu, S., Zhang, D., & Verspoor, K. (2024). Graph transformers: A survey. arXiv preprint arXiv:2407.09777. [4] Yu, Chao, Jiming Liu, Shamim Nemati, and Guosheng Yin. 2023. “Reinforcement Learning in Healthcare: A Survey.” ACM Computing Surveys. https://doi.org/10.114