Sepsis Sequence Generation Method Based on Sequential Coupled Adversarial Learning

Sepsis is a critical condition caused by infection and is a leading cause of death in Intensive Care Units (ICUs). However, in the context of sepsis treatment, actual clinical data are challenging to obtain. To address this challenge, a Sequentially Coupled medical Wasserstein Generative Adversarial Network (SC-med WGAN) with a gradient penalty is proposed in this study. In contrast to existing models that focus on single-step generation, this model emphasizes the sequential generation of sepsis patient statuses and drug doses to improve the simulation of the process of generating clinical data. The SC-med WGAN consists of two coupled generators that coordinate the generation of patient status and drug dose in a unified model. Moreover, the model employs a mixed-loss technique that introduces feature-matched loss and Pearson's correlation coefficient as additional terms to account for the actual distribution of individual variables and the correlation between variables over time. Finally, the model is tested on the Medical Information Mark for Intensive Care-III (MIMIC-III) dataset, which contains 17 898 sepsis patient records. Additionally, the model is validated using anemia data, further demonstrating its accuracy and robustness. The experimental results show that the data generated sequentially by the proposed model are superior to those generated by other models in terms of quality and authenticity. The proposed method reveals a significant interaction between the generation of patient status and drug dose data.