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

Show full item record

Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/40557

Title: Medical Decision Making: A Machine Learning Framework for Classification in Medicine and Biology
Author: Lee, Eva K.
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).
Description: Presented on August 9, 2011 from 8:30-9:30 a.m. in the IBB Building room 1128 on the Georgia Tech campus. Runtime: 73:34 minutes
Type: Lecture
Video
URI: http://hdl.handle.net/1853/40557
Date: 2011-08-09
Contributor: Georgia Institute of Technology. Institute for Bioengineering and Bioscience
Georgia Institute of Technology. School of Industrial and Systems Engineering
Relation: Petit Institute Breakfast Club Seminar Series
Publisher: Georgia Institute of Technology
Subject: Alzheimer's disease
Cancer detection
Metabolomics
Predictive modeling
Systems biology

All materials in SMARTech are protected under U.S. Copyright Law and all rights are reserved, unless otherwise specifically indicated on or in the materials.

Files in this item

Files Size Format View Description
lee.mp4 198.5Mb MPEG-4 video View/ Open Download Video
lee_streaming.html 934bytes HTML View/ Open Streaming Video

This item appears in the following Collection(s)

Show full item record