Identifying pertinent cohorts and addressing imbalance for robust intensive care survival analysis

Conducting survival analysis on Intensive Care Unit (ICU) data enables us to understand how various factors affect patients’ survival time, guiding clinical decisions and treatment plans. Current ICU analysis algorithms generally lack robustness and potentially lead to erroneous and misleading outcomes as they struggle to manually identify small yet effective subsets within large long-tail clinical datasets. Traditionally, the Synthetic Minority Oversampling Technique (SMOTE) is simple and powerful for tabular data but has difficulty generating discrete features. Furthermore, current generative-based balancing methods often perform poorly compared to SMOTE and easily experience mode collapse, which is crucial for ICU analysis. To address these issues, this study first introduces a novel cohort prioritization technique to shortlist effectively relevant cohorts. Cohort Prioritization is a feature-selective approach that identifies pertinent cohorts within the dataset based on the proposed index score system. To tackle the imbalance issue, we proposed an innovative Conditional Generative Adversarial Network (cGAN), cGAN-based SMOTE improved approach called Intensive Care Data Balancer (ICD-Balancer), leveraging the power of generative adversarial networks to rebalance class distributions, where we employed Gumbel-Softmax and proposed Gumbel-Sigmoid to leverage generating multimodal tabular features thereby enhancing the overall performance of classification tasks. The comparative results demonstrate the effectiveness of our proposed approach in resolving class imbalance and the utility of our index scoring system in enhancing feature selection processes. This research advances the state-of-the-art in optimizing feature selection and addressing the class imbalance, with potential applications across diverse tabular medical data requiring robust and interpretable data analysis.