| dc.contributor.advisor | Michael Stonebraker. | en_US |
| dc.contributor.author | Zhou, Erica,M. Eng.(Massachusetts Institute of Technology) | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.coverage.spatial | n-us--- | en_US |
| dc.date.accessioned | 2021-01-06T18:31:10Z | |
| dc.date.available | 2021-01-06T18:31:10Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/129142 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 | en_US |
| dc.description | Cataloged from student-submitted PDF of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 71-73). | en_US |
| dc.description.abstract | In the medical setting, prevention of healthcare-acquired infections (HAIs) is an important area of research in order to mitigate extraneous complications and medical costs. In the United States alone, infections caused by the Clostridioides difficile (C. difi) bacteria affect up to 500,000 hospital patients annually and can have serious, even fatal, consequences. C. difi can spread person to person through direct contact as well as indirect contact through infected surfaces, equipment, or people. However, through current means, it is difficult to quantify exactly how infection is transmitted and, consequently, how to best manage and combat it. Using Kyrix, a pan-and-zoom data visualization platform, we propose an interactive visualization system to depict connections between hospital patients that may be used to identify possible routes of transmission such as shared hospital rooms, reusable medical equipment, and common providers. By taking advantage of the "zoom" functionality, users can access the data at various levels of specificity, starting with a 3D overview of the hospital, zooming into specific floors or rooms of interest, learning about individual patient cases, and finally honing in on a specific patient and their connections with other patients. With such a system, hospital epidemiologists can quickly and easily identify interactions that may represent opportunities for person to person transmission and focus their efforts, investigations, and interventions accordingly. | en_US |
| dc.description.statementofresponsibility | by Erica Zhou. | en_US |
| dc.format.extent | 73 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Interactive visualization and discovery of possible transmission routes of Clostridioides difficile | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1227276694 | en_US |
| dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2021-01-06T18:31:09Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |