According to Khan et al, “a review earns the adjective systematic if it is based on a clearly formulated question, identifies relevant studies, appraises their quality and summarizes the evidence by use of explicit methodology”. Conducting systematic reviews tend to be resource intensive and may suffer from problems such as publication bias, time-lag bias, duplicate bias, citation bias, and outcome reporting bias. This research aims to develop a system to facilitate the creation of systematic reviews. Starting with a clinical question, the proposed system will query ClinicalTrial.gov to search published RCTs. The system will exploit advanced data analytics techniques to systematically mine clinical trials obtained from the ClinicalTrial.gov. From the theoretical perspective, the system provides context for exploring the feasibility and efficacy of using advanced analytics techniques for generating machine readable, real time medical evidence. From a practical perspective, the system is expected to produce cost efficient medical evidence.
Timsina, Prem; El-Gayar, Omar F.; and Nawar, Nevine, "Leveraging Advanced Analytics to Generate Dynamic Medical Systematic Reviews" (2014). Faculty Research & Publications. 170.