Abstract
Disease diagnosis can provide crucial information for clinical decisions that influence the outcome in acute serious illness, and this is particularly in the intensive care unit (ICU). However, the central role of diagnosis in clinical practice is challenged by evidence that does not always benefit patients and that factors other than disease are important in determining patient outcome. To streamline the diagnostic process in daily routine and avoid misdiagnoses, in this paper, we proposed a deep multi-source multi-task attention model (DMMAM) for ICU disease diagnosis. DMMAM exploits multi-sources information from various types of complications, clinical measurements, and the medical treatments to support the diagnosis. We evaluate the proposed model with 50 diseases of 9 classifications on an extensive collection of real-world ICU Electronic Health Records (EHR) dataset with 151729 ICU admissions from 46520 patients. Experiments results demonstrate the effectiveness and the robustness of our model.
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Notes
- 1.
Data available at https://mimic.physionet.org/.
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Acknowledgement
This work was supported by the Nature Science Foundation of Jilin Province (20180101330JC, 20190302029GX), the Fundamental Research Funds for the Central Universities (No. 2412017QD028), the China Postdoctoral Science Foundation (No. 2017M621192), the Scientific and Technological Development Program of Jilin Province (No. 20180520022JH, 20190302109GX). The authors also gratefully acknowledge the financial support from China Scholarship Council (No. 201706170617).
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Shi, Z., Zuo, W., Chen, W., Yue, L., Hao, Y., Liang, S. (2019). DMMAM: Deep Multi-source Multi-task Attention Model for Intensive Care Unit Diagnosis. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_4
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