A Clinical Data Based Framework for Outcome Forecasting in Patients With Pneumonia

Respiratory diseases are a major cause of death globally, placing a significant burden on healthcare services. Early-stage clinical decision-making is crucial for enabling personalized, prioritized treatment and more efficient allocation of healthcare resources. Clinicians can intervene proactively and develop appropriate treatment plans for patients when provided with vital information such as mortality prediction, deterioration detection, and length-of-stay prediction. To precisely predict such vitals, it is essential to leverage sequential information that is inherent in clinical variables. In this paper, we employ a unified framework for patient outcome forecasting in pneumonia patients. The proposed model utilizes clinical time-series data of varying lengths, along with static admission information, to effectively capture the sequential information of clinical variables. Additionally, we model the imbalanced distribution of mortality prediction and deterioration detection through weight constraints, and we account for the right-skewed distribution of length-of-stay data to enhance the robustness of the model. Furthermore, we develop a data splitting strategy to track dynamic changes in model performance at different timestamps, helping to bridge the gap between testing conditions and real-world scenarios. We conduct experiments on CAP-AI dataset that was obtained and collected from the University Hospitals of Leicester with the involvement of clinicians. It is based on real-world clinical data from patients admitted with pneumonia-related diagnoses. Extensive experimental results demonstrate the effectiveness and robustness of our approach whilst predicting patient outcomes in a clinical setting.