Medical Decision Making: A Machine Learning Framework for Classification in Medicine and Biology

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dc.contributor.author Lee, Eva
dc.date.accessioned 2011-08-11T19:18:37Z
dc.date.available 2011-08-11T19:18:37Z
dc.date.issued 2011-08-09
dc.identifier.uri http://hdl.handle.net/1853/40557
dc.description Presented on August 9, 2011 from 8:30-9:30 a.m. in the IBB Building room 1128 on the Georgia Tech campus. en_US
dc.description Runtime: 73:34 minutes
dc.description.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). en_US
dc.format.extent 73:34 minutes
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Alzheimer's disease en_US
dc.subject Cancer detection en_US
dc.subject Metabolomics en_US
dc.subject Predictive modeling en_US
dc.subject Systems biology en_US
dc.title Medical Decision Making: A Machine Learning Framework for Classification in Medicine and Biology en_US
dc.type Lecture en_US
dc.type Video en_US
dc.contributor.corporatename Georgia Institute of Technology. Institute for Bioengineering and Bioscience
dc.contributor.corporatename Georgia Institute of Technology. School of Industrial and Systems Engineering


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