Jay Jarman
Biographic Data
| ID | 6072811 |
|---|---|
| NAME | Jay Jarman |
| GIVEN NAMES | Jay |
| FAMILY NAME | Jarman |
| SIGNATURE | JARMAN J |
| AFFILIATIONS | Stephen L. Luther, James A. McCart, Bridget Hahm, Dezon Finch, Philip R. Foulis, William A. Lapcevic, Robert R. Campbell, and Gail Powell-Cope are with the HSR&D Center of Innovation on Disability and Rehabilitation Research, James A. Haley Veterans Hospital, Tampa, FL. Donald J. Berndt is with the University of South Florida College of Business Administration, Tampa. Jay Jarman is with the East Tennessee State University Department of Computing, Johnson City. Ronald I. Shorr is with the North... |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2015 |
| H-INDEX | 0 |
Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining
Objectives. We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. Methods. We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 vet…
A Technological Tool to Detect Plagiarized Projects in Microsoft Access
Over one in ten students surveyed have admitted to copying programs in courses with computer assignments. The ease with which digital coursework can be copied and the impracticality of manually checking for plagiarized projects in large courses has only compounded the problem. As current research has focused predominantly on detecting plagiarism for textual applications such as source code and documents, there exists a gap in detecting plagiarism…
No prominent works on this page.
A Technological Tool to Detect Plagiarized Projects in Microsoft Access
Over one in ten students surveyed have admitted to copying programs in courses with computer assignments. The ease with which digital coursework can be copied and the impracticality of manually checking for plagiarized projects in large courses has only compounded the problem. As current research has focused predominantly on detecting plagiarism for textual applications such as source code and documents, there exists a gap in detecting plagiarism…
Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining
Objectives. We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. Methods. We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 vet…
Academic integrity and plagiarism (1 work) · Ambulatory (1 work) · Ambulatory care (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Chart (1 work) · Computer Science (1 work) · Copying (1 work) · Coursework (1 work) · Emergency Medicine (1 work)