Abstract
Personalized medicine (PM) aiming at tailoring medical treatment to individual patient is critical in guiding precision prescription. An important challenge for PM is comorbidity due to the complex interrelation of diseases, medications and individual characteristics of the patient. To address this, we study the problem of PM for comorbidity and propose a neural network framework Deep Personalized Prescription for Comorbidity (PPC). PPC exploits multi-source information from massive electronic medical records (EMRs), such as demographic information and laboratory indicators, to support personalized prescription. Patient-level, disease-level and drug-level representations are simultaneously learned and fused with a trilinear method to achieve personalized prescription for comorbidity. Experiments on a publicly real world EMRs dataset demonstrate PPC outperforms state-of-the-art works.
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Notes
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We have also examined LSTM and other activation functions to learn to represent diagnosis, but they have less efficiency and worse performance.
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We have examined both l1-norm and l2-norm, and find their performance are similar.
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Acknowledgements
This work was partially supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000904, NSFC (61702190), NSFC-Zhejiang (U1609220), Shanghai Sailing Program (17YF1404500) and SHMEC (16CG24).
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Wang, L., Zhang, W., He, X., Zha, H. (2018). Personalized Prescription for Comorbidity. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_1
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