LSTM-based Pulmonary Air Leak Forecasting for Chest Tube Management

Prolonged air leak is a complication arising from a collapsed lung which can lead to serious illness such as pneumonia and empyema, and patient suffering from indwelling chest tubes. Drainage of air and liquid from chest drains can be monitored and recorded using novel digital chest drainage devices. The collected data can be analyzed by predictive models, which can provide decision support in chest tube management. Despite the promising adoption of predictive models in this context, existing approaches are still in their infancy and are mostly based on autoregressive and conventional machine learning models. In this paper, we present a LSTM-based model architecture for air leak forecasting that is able to deal with non-linear dependencies among different features and contiguous time points. We devise a post-processing procedure that leverages predictions to suggest whether the patient could have their chest tube safely removed in the upcoming hours, and evaluate the results according to a medical protocol. Experimental results show that our model is able to outperform currently adopted models, in terms of both forecasting and classification performance, suggesting the feasibility of our approach for chest tube management.