Kuang Zhaobin, Thomson James, Caldwell Michael, Peissig Peggy, Stewart Ron, Page David
University of Wisconsin.
Morgridge Institute.
KDD. 2016 Aug;2016:491-500. doi: 10.1145/2939672.2939715.
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial evaluation, we look for drugs that can control Fasting Blood Glucose (FBG) level in our experiments. Applying CSCCS to the Marshfield Clinic EHR, well-known drugs that are indicated for controlling blood glucose level are rediscovered. Furthermore, some drugs with recent literature support for the potential effect of blood glucose level control are also identified.
计算药物重新定位（CDR）是通过挖掘大规模异构药物相关数据源来发现现有药物潜在新适应症的任务。利用电子健康记录（EHR）中提供的数字生理测量值和各种药物处方之间的患者级时间顺序信息，我们提出了一种用于CDR的连续自我对照病例系列（CSCCS）模型。作为初步评估，我们在实验中寻找能够控制空腹血糖（FBG）水平的药物。将CSCCS应用于马什菲尔德诊所的EHR，重新发现了用于控制血糖水平的知名药物。此外，还识别出了一些近期文献支持对血糖水平控制有潜在作用的药物。
