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Medical Decision Making: A Machine Learning Framework for Classification in Medicine and Biology
Presented on August 9, 2011 from 8:30 a.m.-9:30 a.m. at the Parker H. Petit Institute for Bioengineering & Bioscience (IBB), room 1128, Georgia Tech.Runtime: 73:34 minutesSystems 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)