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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Personalized Prescription for Comorbidity

verfasst von : Lu Wang, Wei Zhang, Xiaofeng He, Hongyuan Zha

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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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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Nächstes Kapitel Modeling Patient Visit Using Electronic Medical Records for Cost Profile Estimation
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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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Metadaten
Titel
Personalized Prescription for Comorbidity
verfasst von
Lu Wang
Wei Zhang
Xiaofeng He
Hongyuan Zha
Copyright-Jahr
2018
Buch
Database Systems for Advanced Applications

Print ISBN: 978-3-319-91457-2

Electronic ISBN: 978-3-319-91458-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-91458-9_1