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Personalized Prescription for Comorbidity

  • Lu Wang
  • Wei Zhang
  • Xiaofeng HeEmail author
  • Hongyuan Zha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

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.

Keywords

Personalized prescription Deep learning Multi-source fusion Comorbidity 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Georgia Institute of TechnologyAtlantaUSA

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