Preliminary Result on Finding Treatments for Patients with Comorbidity

  • Yuanlin ZhangEmail author
  • Zhizheng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8903)


According to some research, comorbidity is reported in 35 to 80 % of all ill people [1]. Multiple guidelines are needed for patients with comorbid diseases. However, it is still a challenging problem to automate the application of multiple guidelines to patients because of redundancy, contraindicated, potentially discordant recommendations. In this paper, we propose a mathematical model for the problem. It formalizes and generalizes a recent approach proposed by Wilk and colleagues. We also demonstrate that our model can be encoded, in a straightforward and simple manner, in Answer Set Programming (ASP) – a class of Knowledge Representation languages. Our preliminary experiment also shows our ASP based implementation is efficient enough to process the examples used in the literature.


Answer set programming Clinical practice guidelines Knowledge representation Comorbidity 



We would like to thank Michael Gelfond and Samson Tu for discussions on this subject. Yuanlin Zhang’s work is partially supported by the NSF grants IIS-1018031 and CNS-1359359. Zhizheng Zhang’s work is partially supported by Project 60803061 and 61272378 sponsored by National Natural Science Foundation of China, and Project BK2008293 by Natural Science Foundation of Jiangsu.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Texas Tech UniversityLubbockUSA
  2. 2.Southeast UniversityNanjingChina

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