@inproceedings{0efc6971912a4acfabecda3928848109,
title = "Personalized prescription for comorbidity",
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 = "Comorbidity, Deep learning, Multi-source fusion, Personalized prescription",
author = "Lu Wang and Wei Zhang and Xiaofeng He and Hongyuan Zha",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018 ; Conference date: 21-05-2018 Through 24-05-2018",
year = "2018",
doi = "10.1007/978-3-319-91458-9\_1",
language = "英语",
isbn = "9783319914572",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--19",
editor = "Jian Pei and Shazia Sadiq and Jianxin Li and Yannis Manolopoulos",
booktitle = "Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings",
address = "德国",
}