Abstract
Early and accurate prediction of sepsis could help physicians with proper treatments and improve patient outcomes. We present a deep learning framework built on a bidirectional long short-term memory (BiLSTM) network model to identify septic patients in the intensive care unit (ICU) settings. The fixed value data padding method serves as an indicator to maintain the missing patterns from the ICU records. The devised masking mechanism allows the BiLSTM model to learn the informative missingness from the time series data with missing values. The developed method can better solve two challenging problems of data length variation and information missingness. The quantitative results demonstrated that our method outperformed the other state-of-the-art algorithms in predicting the onset of sepsis before clinical recognition. This suggested that the deep learning based method could be used to assist physicians for early diagnosis of sepsis in real clinical applications.
This work was partially supported by the National Natural Science Foundation of China (61876197), and the Beijing Municipal Natural Science Foundation (7192105).
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Zhao, R., Wan, T., Li, D., Zhang, Z., Qin, Z. (2020). A Deep Learning Model for Early Prediction of Sepsis from Intensive Care Unit Records. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_90
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