S1: Stratification of Asthma-like Patients
Stratification of Asthma-like Patients Using Clinical Records and Exposures
UNC Chapel Hill - RENCI
Some estimates place the cost of bringing a drug to market at one billion U.S dollars and hence reducing the cost and length of clinical trials can indirectly lower healthcare costs. One source of clinical trial failure, lack of efficacy and safety, could be mitigated through decision support for patient stratification. As part of the NIH-funded Biomedical Data Translator project, we are integrating multiple, previously disparate datasets, which is empowering investigators with new tools for data-driving patient subtyping. For example, through the Data Translator project, we can combine clinical records with exposure data in support of powerful models for classification. We have implemented supervised and unsupervised machine learning models on these data for predicting patient outcomes according to exposure in order to better understand patient disease and response. Here we will discuss methods and some preliminary results.