Automatically Creating and Updating Meta-Studies of Randomized Controlled Trials

DESCRIPTION provided by applicant A andquot meta studyandquot or andquot meta analysisandquot collects and analyzes many studies on the same topic to understand if there is a meaningful overall result Meta studies can support or refute interventions spur new investigations and lead to novel clinical guidelines However constructing meta studies is a time intensive process of searching the literature compiling the results and performing the statistical analysis Due to the time commitment that is required many topics are unexplored and many meta studies are not kept up to date with the latest published results Finally a number of unknown biases via subjective choices during the meta study may influence the results Our long term goal is to automate as much as possible the meta study process This should decrease subjective bias increase the dissemination of evidence especially for diseases and interventions that receive less attention and allow for the automatic updating of meta studies as new results are published We propose a computer system that uses statistical machine learning to gather and group studies focused on similar interventions and outcomes extract the necessary results from the text and analyze the results using standard meta analysis techniques The final output will be presented in a spreadsheet like Web interface where users can explore and even change the data and meta analyses Our team uniquely blends technical expertise in machine learning with leadership in publishing meta studies about Inflammatory Bowel Disease IBD our disease of focus for our Phase I feasibility study We are therefore qualified technically and able to ensure that the techniques generate valid and accurate meta studies Our Phase I results will define the current state of the art for this novel task Further although we will initially focus n IBD our Phase I results will demonstrate that our approach can generalize to other diseases eventually applying to any intervention and any disease The feasibility shown by our Phase I results will motivate our Phase II effort where we will focus on dramatically improving the approach yielding broad coverage of all medical literature and generating human quality meta studies We note that by the end of Phase I we should have a viable end to end prototype focused on IBD which we can begin taking to market The final product should significantly benefit our target markets given the Phase II emphasis to improve the technology user experience and scope of covered diseases PUBLIC HEALTH RELEVANCE A meta analysis collects and analyzes the results from multiple studies that are all focused on the same topic and it can confirm or refute the overall effect across the studies lead to changes in clinical guidelines or spur new directions for research However generating a meta analysis is an extremely time consuming process so many diseases are not covered and most meta analyses are not updated to reflect the latest published studies This work begins to automate the process of creating meta analyses overcoming these difficulties in order to make the results published in the medical literature more accessible