Guangzhi Qu publishes in Neurocomputing

Assistant Professor Guangzhi Qu, of the Computer Science and Engineering Department, recently published a paper on Local Analgesia Adverse Effects Prediction Using Multi-label Classification (Neurocomputing, Volume 92, Pages 18-27, 2012). Coauthors on this study include Hui Wu, Qu's former graduate student in the Computer Science and Informatics PhD program (now a software engineer at Yahoo!), and Craig Hartrick of Beaumont Health System and the Oakland University William Beaumont School of Medicine. The abstract of the paper is given below.

It is desirable to determine minimal effective initial local anesthetic bolus required to provide satisfactory analgesia following surgery. A way to predict potential adverse effects based on the type of anesthetic and initial bolus amount administered would be a significant contribution to presonalized medicine. In this work, we propose new methods for multi-label classification to predict adverse effects in order to help doctors make appropriate treatment decisions. In this endeavor, the Pair-Dependency Multi-Label Bayesian Classifier (PDMLBC) and Complete-Dependency Multi-Label Bayesian Classifier (CDMLBC) models are proposed as classifiers that take into account the impact of features on the dependency between labels. We evaluated the proposed models on 36 patients who had recently received arthroscopic shoulder surgery. The experimental results show that the CDMLBC model outperforms other existing methods in multi-label classification.

The collaboration between Qu and Hartrick is an example of research catalyzed by an OU-Beaumont Multidisciplinary Research Award. They presented preliminary results of their research at the OU-Beaumont Biomedical Research Symposium in February, 2011.