Probabilistic Modeling of Missing Data to Improve Predictions Using Metabolomics Data

Research Overview

Dr. Christopher Gillies (Emergency Medicine) and the team develop statistical methods, especially Bayesian methods, that can correctly take into account missing data when they examine patient metabolomics data. They will use such methods to predict the survival of sepsis patients, and to detect the onset of acute respiratory distress syndrome.

Research Impact

Research Team

Christopher E. Gillies, PhD, Principal Investigator, Department of Emergency Medicine

Kevin Ward, MD, Co-Principal Investigator, Department of Emergency Medicine and Department of Biomedical Engineering

Kathleen Stringer, PhamD, Co-Principal Investigator, Department of Pharmacy

Xudong Fan, PhD, Co-Principal Investigator, Department of Biomedical Engineering

Ruchi Sharma, PhD, Co-Investigator, Department of Biomedical Engineering

Theodore Jennaro, PharmD, Student Investigator, Department of Clinical Pharmacy