Intensity Estimation for Non-Homogeneous Poisson Processes Used to Model Real-Time Medication Event Monitor
Yan Wang
University of California at Los Angeles
Marc Rosen
Yale University
Jie Shen
University of California at Los Angeles
Brent Moore
Yale University
Eric Daar
HarborUCLA Medical Center
Honghu Liu
University of California, Los Angeles
It is challenge to model medication taking behavior and eventually to predict the change of behavior, especially for those vulnerable patients (e.g. substance abusers). With the help of modern technology (e.g. Wisepill device), we are able to monitor the medication taking behavior in real time. In the meanwhile, after a few minutes delay of detecting the events and analyzing the events, a real time intervention can be generated to communicate with patients directly by the packages of Python, such as text messages to the mobile phone of the patients. The statistical methods that can be used to model the complexity of the medication taking events and to predict the future missed dosage are important for designing the personalized module of the device and therefore personalized intervention. This intervention not only will improve the medication adherence, but the personalized intervention will help to build up the correct medication behavior in the future. In this talk, we use Non-Homogeneous Poisson Process (NHPP) to model the sequence of medication taking during the day.