Institute for Software Research
School of Computer Science, Carnegie Mellon University
Predictions for Biomedical Decision Support
Specifically, I carefully investigated two representative problems, bioterrorism-related disease outbreak and personalized clinical decision support, for which previous research does not provide satisfactory solutions. I developed a Temporal Maximum Margin Markov Network framework to consider the temporal correlation concurrently with relational dependency in bioterrorism-related diseases‚ outbreaks. This framework reduces the ambiguity in estimating outcome variables from noisy manifestations by considering complementary information. It outperformed state-of the-art models with synthetic and real world datasets, and improved average state prediction accuracy in predicting simulated biohazards. Regarding personalized clinical decision support, I focused on an important but little-studied measurement "calibration," which stratifies how outcomes affect various genetic population groups within a patientdiagnosis population. I designed joint optimization framework to combine discrimination and calibration, and demonstrated models (DP-SVM, SIO and AC-LR) developed under this multitargeted framework perform better on both metrics than single-targeted models. I conducted various real data experiments including Hospital Discharge Error, Myocardial Infarction and Breast Cancer Gene Expression Data to verify the efficacy of my joint optimization framework.