Ying Li, Ping Zhang, et al.
AMIA Annual Symposium
The proliferation of Electronic Health Records (EHRs) challenges data miners to discover potential and previously unknown patterns from a large collection of medical data. One of the tasks that we address in this paper is to reveal previously unknown effects of drugs on laboratory test results. We propose a method that leverages drug information to find a meaningful list of drugs that have an effect on the laboratory result. We formulate the problem as a convex non smooth function and develop a proximal gradient method to optimize it. The model has been evaluated on two important use cases: lowering low-density lipoproteins and glycated hemoglobin test results. The experimental results provide evidence that the proposed method is more accurate than the state-of-the-art method, rediscover drugs that are known to lower the levels of laboratory test results, and most importantly, discover additional potential drugs that may also lower these levels.
Ying Li, Ping Zhang, et al.
AMIA Annual Symposium
Ioakeim Perros, Fei Wang, et al.
SDM 2017
Hui Huang, Ping Zhang, et al.
Scientific Reports
Ping Zhang, Fei Wang, et al.
AMIA Annual Symposium proceedings