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dc.contributor.authorLee, Eva K.
dc.date.accessioned2011-08-11T19:18:37Z
dc.date.available2011-08-11T19:18:37Z
dc.date.issued2011-08-09
dc.identifier.urihttp://hdl.handle.net/1853/40557
dc.descriptionPresented 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.en_US
dc.descriptionRuntime: 73:34 minutes
dc.description.abstractSystems 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).en_US
dc.format.extent73:34 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesPetit Institute Breakfast Club Seminar Series
dc.subjectAlzheimer's diseaseen_US
dc.subjectCancer detectionen_US
dc.subjectMetabolomicsen_US
dc.subjectPredictive modelingen_US
dc.subjectSystems biologyen_US
dc.titleMedical Decision Making: A Machine Learning Framework for Classification in Medicine and Biologyen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Bioengineering and Bioscience
dc.contributor.corporatenameGeorgia Institute of Technology. School of Industrial and Systems Engineering


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