Multimodal Forecasting of Sparse Intraoperative Hypotension Events Powered by Language Model

Intraoperative hypotension (IOH) frequently occurs under general anesthesia and is strongly linked to adverse outcomes such as myocardial injury and increased mortality. Despite its significance, IOH prediction is hindered by event sparsity and the challenge of integrating static and dynamic data across diverse patients. In this paper, we propose \textbf{IOHFuseLM}, a multimodal language model framework. To accurately identify and differentiate sparse hypotensive events, we leverage a two-stage training strategy. The first stage involves domain adaptive pretraining on IOH physiological time series augmented through diffusion methods, thereby enhancing the model sensitivity to patterns associated with hypotension. Subsequently, task fine-tuning is performed on the original clinical dataset to further enhance the ability to distinguish normotensive from hypotensive states. To enable multimodal fusion for each patient, we align structured clinical descriptions with the corresponding physiological time series at the token level. Such alignment enables the model to capture individualized temporal patterns alongside their corresponding clinical semantics. In addition, we convert static patient attributes into structured text to enrich personalized information. Experimental evaluations on two intraoperative datasets demonstrate that IOHFuseLM outperforms established baselines in accurately identifying IOH events, highlighting its applicability in clinical decision support scenarios. Our code is publicly available to promote reproducibility at https://github.com/zjt-gpu/IOHFuseLM.

HMF: A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction

Intraoperative hypotension (IOH) prediction using Mean Arterial Pressure (MAP) is a critical research area with significant implications for patient outcomes during surgery. However, existing approaches predominantly employ static modeling paradigms that overlook the dynamic nature of physiological signals. In this paper, we introduce a novel Hybrid Multi-Factor (HMF) framework that reformulates IOH prediction as a blood pressure forecasting task. Our framework leverages a Transformer encoder, specifically designed to effectively capture the temporal evolution of MAP series through a patch-based input representation, which segments the input physiological series into informative patches for accurate analysis. To address the challenges of distribution shift in physiological series, our approach incorporates two key innovations: (1) Symmetric normalization and de-normalization processes help mitigate distributional drift in statistical properties, thereby ensuring the model's robustness across varying conditions, and (2) Sequence decomposition, which disaggregates the input series into trend and seasonal components, allowing for a more precise modeling of inherent sequence dependencies. Extensive experiments conducted on two real-world datasets demonstrate the superior performance of our approach compared to competitive baselines, particularly in capturing the nuanced variations in input series that are crucial for accurate IOH prediction.