Eva Lee, PhD - Director, Center for Operations Research in Medicine and HealthCare, Professor, School of Industrial and Systems Engineering

Abstract

Systems modeling and quantitative analysis of large amounts of complex clinical and biological data may help to identify discriminatory patterns that can uncover health risks, detect early disease formation, monitor treatment and prognosis, and predict treatment outcome. In this talk, we describe a machine-learning framework for medical decision making. It consists of a pattern recognition module, a feature selection module, and a classification modeler and solver. The pattern recognition module involves automatic image analysis, genomic pattern recognition, and spectrum pattern extractions. The feature selection module consists of a combinatorial selection algorithm where discriminatory patterns are extracted from among a large set of pattern attributes. These modules are wrapped around the classification modeler and solver into a machine learning framework. The classification modeler and solver consist of novel optimization-based predictive models that maximize the correct classification while constraining the inter-group misclassifications. The classification/predictive models 1)have the ability to classify any number of distinct groups; 2) allow incorporation of heterogeneous, and continuous/time-dependent types of attributes as input; 3) utilize a high-dimensional data transformation that minimizes noise and errors in biological and clinical data; 4) incorporate a reserved-judgement region that provides a safeguard against over-training; and 5) have successive multi-stage classification capability.

Successful applications of our model to developing rules for gene silencing in cancer cells, predicting the immunity of vaccines, identifying the cognitive status of individuals, and predicting metabolite concentrations in humans will be discussed. We acknowledge our clinical/biological collaborators: Dr. Vertino (Winship Cancer Institute, Emory), Drs. Pulendran and Ahmed (Emory Vaccine Center), Dr. Levey (Neurodegenerative Disease and Alzheimer’s Disease), and Dr. Jones (Clinical Biomarkers, Emory).

The IBB Breakfast Club seminar series was started with the spirit of the Institute's interdisciplinary mission in mind. The goal of the seminar series is to highlight research taking place throughout the institute to enable the IBB community to further collaborative opportunities and interdisciplinary research. Faculty are often asked to speak at other universities and conferences, but rarely present at their home institution, this seminar series is an attempt to close that gap. The IBB Breakfast Club is open to anyone in the bio-community.

Continental breakfast and coffee will be served.