LSTM-based Pulmonary Air Leak Forecasting for Chest Tube Management
Published in IEEE International Conference on Big Data, 2022
We present a LSTM-based model architecture for air leak forecasting that captures non-linear dependencies among features and time points. The model achieved 97.4% sensitivity and 81% accuracy in predicting patient resolution, outperforming autoregressive baselines for clinical decision support in chest tube management.
Recommended citation: R. Corizzo, R. Yepez-Lopez, S. Gilbert, N. Japkowicz. "LSTM-based Pulmonary Air Leak Forecasting for Chest Tube Management." IEEE Big Data 2022.
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