Abstract
One of the most crucial tasks in the ICU is mortality prediction. The number of deceased patients is significantly lower than the number of survivors, and it is simple to over-identify the survivors. Additionally, the clinical use of present machine learning and deep learning models is challenging due to their lack of interpretability. To address the aforementioned issues, we innovatively propose the Interpretable Conditional Augmentation Classification (ICAC) method. By using CWGAN to create balanced samples, ICAC learns the distribution of minor samples. In order to make better clinical suggestions, the Shapley value is utilized to examine the marginal contribution of patient characteristics to the prediction model. We test the model on the latest released MIMIC-IV, and the experimental results show that the AUC index of our model is superior than that of the basic model. Our proposed method can successfully address the class imbalance issue in EHRs, clarify how features affect model outcomes, and offer useful recommendations for clinical practice.
Supported by National Key R&D Program of China (2018AAA0101003) and National Natural Science Foundation of China (Grant No. 71901050).
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Li, T., Yin, N., Gao, P., Li, D., Lu, W. (2022). An Interpretable Conditional Augmentation Classification Approach for Imbalanced EHRs Mortality Prediction. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_29
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