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Full-Text Articles in Physical Sciences and Mathematics
Data Mining Revision Controlled Document History Metadata For Automatic Classification, Dustin Maass
Data Mining Revision Controlled Document History Metadata For Automatic Classification, Dustin Maass
Theses and Dissertations
Version controlled documents provide a complete history of the changes to the document, including everything from what was changed to who made the change and much more. Through the use of cluster analysis and several sets of manipulated data, this research examines the revision history of Wikipedia in an attempt to find language-independent patterns that could assist in automatic page classification software. Utilizing two sample data sets and applying the aforementioned cluster analysis, no conclusive evidence was found that would indicate that such patterns exist. Our work on the software, however, does provide a foundation for more possible types of …
Extraction And Classification Of Drug-Drug Interaction From Biomedical Text Using A Two-Stage Classifier, Majid Rastegar-Mojarad
Extraction And Classification Of Drug-Drug Interaction From Biomedical Text Using A Two-Stage Classifier, Majid Rastegar-Mojarad
Theses and Dissertations
One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, …
Regularization Methods For Predicting An Ordinal Response Using Longitudinal High-Dimensional Genomic Data, Jiayi Hou
Theses and Dissertations
Ordinal scales are commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. Notable examples include cancer staging, which is a five-category ordinal scale indicating tumor size, node involvement, and likelihood of metastasizing. Glasgow Coma Scale (GCS), which gives a reliable and objective assessment of conscious status of a patient, is an ordinal scaled measure. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical ordinal modeling methods based on the likelihood approach have contributed to the analysis of data in …
Assessment And Prediction Of Cardiovascular Status During Cardiac Arrest Through Machine Learning And Dynamical Time-Series Analysis, Sharad Shandilya
Assessment And Prediction Of Cardiovascular Status During Cardiac Arrest Through Machine Learning And Dynamical Time-Series Analysis, Sharad Shandilya
Theses and Dissertations
In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viability for decision-support in clinical settings. The approach, which focuses on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic …
Geometric Approach To Support Vector Machines Learning For Large Datasets, Robert Strack
Geometric Approach To Support Vector Machines Learning For Large Datasets, Robert Strack
Theses and Dissertations
The dissertation introduces Sphere Support Vector Machines (SphereSVM) and Minimal Norm Support Vector Machines (MNSVM) as the new fast classification algorithms that use geometrical properties of the underlying classification problems to efficiently obtain models describing training data. SphereSVM is based on combining minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three speeds up the training phase of SVMs significantly and reaches similar (i.e., practically the same) accuracy as the other classification models over several big and large real data sets within the strict validation frame of a double (nested) …
Instrument And Method Development For Single-Cell Classification Using Fluorescence Imaging Multivariate Optical Computing, Joseph Swanstrom
Instrument And Method Development For Single-Cell Classification Using Fluorescence Imaging Multivariate Optical Computing, Joseph Swanstrom
Theses and Dissertations
Multivariate optical computing (MOC) is an all-optical approach of predictive spectroscopy that utilizes multivariate calibration and spectral pattern recognition techniques while operating in a simple filter photometer instrument, removing the need for expensive instrumentation and post-processing of spectral data. This is accomplished with specially designed interference filters called multivariate optical elements (MOEs). MOC can provide analytical solutions for applications requiring low cost, rugged, and simple to operate instrumentation for use in remote and hazardous environments such as open ocean waters. These instrument specifications are central for developing a method for classifying phytoplankton in their natural environment. Phytoplankton are photosynthetic single …