Medicine and Health Sciences Commons^™

Ssrn As An Initial Revolution In Academic Knowledge Aggregation And Dissemination, David Bray, Sascha Vitzthum, Benn Konsynski

Sascha Vitzthum

Within this paper we consider our results of using the Social Science Research Network (SSRN) over a period of 18 months to distribute our working papers to the research community. Our experiences have been quite positive, with SSRN serving as a platform both to inform our colleagues about our research as well as inform us about related research (through email and telephoned conversations of colleagues who discovered our paper on SSRN). We then discuss potential future directions for SSRN to consider, and how SSRN might well represent an initial revolution in 21st century academic knowledge aggregation and dissemination. Our paper …

Text Mining In Radiology Reports, Tianxia Gong, Chew Lim Tan, Tze-Yun Leong, Cheng Kiang Lee, Boon Chuan Pang, C. C. Tchoyoson Lim, Qi Tian, Suisheng Tang, Zhuo Zhang

Research Collection School Of Computing and Information Systems

Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information describing radiologist's observations on the patient's medical conditions in the associated medical images. However as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used, unless proper text mining has been applied. In this paper we propose a text mining system to extract and use the information in radiology reports. The system consists of three main modules: a medical finding extractor a report and image retriever and a text-assisted image feature extractor In evaluation, the …

Towards Self-Organizing, Smart Business Networks: Let’S Create ‘Life’ From Inert Information, David Bray, Benn Konsynski

David A. Bray

We review three different theories that can inform how researchers can determine the performance of smart business networks, to include: (1) the Theory of Evolution, (2) the Knowledge-Based Theory of the Firm, and (3) research insights into computers and cognition. We suggest that each of these theories demonstrate that to be generally perceived as smart, an organism needs to be self-organizing, communicative, and tool-making. Consequentially, to determine the performance of a smart business network, we suggest that researchers need to determine the degree to which it is self-organizing, communicative, and tool-making. We then relate these findings to the Internet and …

Evaluating Online Health Information: Beyond Readability Formulas, Gondy Leroy, Stephen Helmreich, James Cowie, Trudi Miller '08, Wei Zheng '08

CGU Faculty Publications and Research

Although understanding health information is important, the texts provided are often difficult to understand. There are formulas to measure readability levels, but there is little understanding of how linguistic structures contribute to these difficulties. We are developing a toolkit of linguistic metrics that are validated with representative users and can be measured automatically. In this study, we provide an overview of our corpus and how readability differs by topic and source. We compare two documents for three groups of linguistic metrics. We report on a user study evaluating one of the differentiating metrics: the percentage of function words in a …

Volume 01, Jessica Fields, Stephanie Neeley, Derek W. Hambright, Mary E. Lehman, Andrew R. Grzankowski, Zachary Johnson, Boone M. Prentice, Ashley M. Swandby, Victoria Morgan, Katie Williamson, Kristine G. Bender, Katelyn N. Romaine, D. Nicole Swann, Jessica Fox, Mike Mcateer, Alex Grabiec, Laura Nodtvedt, Nick Costa, Rachel Wolfe, Zack Dalton

Incite: The Journal of Undergraduate Scholarship

Introduction from Dean Dr. Charles Ross

Three Decades of Digging: Undergraduate Archeology at Longwood by Jessica Fields and Stephanie Neeley

Interactions of Allelopathy and Heat Stress in Plants by Derek W. Hambright and Mary E. Lehman

Inertial Electrostatic Confinement D-D Fusion Device: Construction and Simulation by Andrew R. Grzankowski

Shackled Nim by Zachary Johnson

Development of GC-MS and Chemometric Methods for the Analysis of Accelerants in Arson Cases by Boone M. Prentice

A Comparison of Image Analysis Methods in cDNA Microarrays by Ashley M. Swandby

Perceived Sexual Activity of Short and Long-Term Relationships by Victoria Morgan and Katie Williamson

Elderly …

Real-Time Ultrasound Simulation For Medical Training And Standardized Patient Assessment, Bo Sun

Computational Modeling & Simulation Engineering Theses & Dissertations

With the increasing role played by ultrasound in clinical diagnostics, ultrasound training in medical education has become more and more important. The clinical routine for ultrasound training is on real patients; therefore monitored and guided examinations involving medical students are quite time-constrained. Furthermore, standardized patients (SPs), who are increasingly used in medical school for teaching and assessing medical students, need to be augmented. These SPs are typically healthy individuals who can not accurately portray the variety of abnormalities that are needed for training especially when medical examinations involve instrument interactions. To augment SPs in a realistically effective way and also …

A Delphi Study Assessing Long-Term Access To Electronic Medical Records (Emr), Byron D. Nicholson

Theses and Dissertations

This research effort addressed the issue of long-term access to electronic medical records as technological generations become obsolete, thereby preventing the access to patient health information. Using the Delphi methodology, experts with experience in electronic medical records and applicable systems provided insight based on their years of hands-on experience managing and/or using records and these systems. The end result of this research was a collection of ideas that medical institutions and medical informaticians must consider to ensure that patients and hospitals do not lose long-term access to electronic medical records as electronic medical records and technology continually evolves. Results of …

Prostate Segmentation On Pelvic Ct Images Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell

Computer Science Faculty Publications and Presentations

A genetic algorithm (GA) for automating the segmentation of the prostate on pelvic computed tomography (CT) images is presented here. The images consist of slices from three-dimensional CT scans. Segmentation is typically performed manually on these images for treatment planning by an expert physician, who uses the “learned” knowledge of organ shapes, textures and locations to draw a contour around the prostate. Using a GA brings the flexibility to incorporate new “learned” information into the segmentation process without modifying the fitness function that is used to train the GA. Currently the GA uses prior knowledge in the form of texture …

Droid: The Drosophila Interactions Database, A Comprehensive Resource For Annotated Gene And Protein Interactions, Jingkai Yu, Svetlana Pacifico, Guozhen Liu, Russell L. Finley Jr

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. …

Object Detection And Classification With Applications To Skin Cancer Screening, Jonathan Blackledge, Dmitryi Dubovitskiy

Articles

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image. The classification method is based on the application of a set of features which include fractal parameters such as the Lacunarity and Fractal Dimension. Thus, the approach used, incorporates the characterisation of an object in terms of its texture.

The principal issues associated with object recognition are presented which includes two novel fast segmentation algorithms for which C++ code is provided. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and …