Title:
Automation of Evidence Matching and Systemic Reviews Using Web-Based Medical Literature
Automation of Evidence Matching and Systemic Reviews Using Web-Based Medical Literature
| dc.contributor.author | Ho, Joyce | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Heath Analytics and Informatics | en_US |
| dc.contributor.corporatename | Emory University. Dept. of Computer Science | en_US |
| dc.date.accessioned | 2020-03-09T17:37:24Z | |
| dc.date.available | 2020-03-09T17:37:24Z | |
| dc.date.issued | 2020-02-25 | |
| dc.description | Presented on February 25, 2020 at 3:00 p.m. in the Jesse W. Mason Building, Room 2117. | en_US |
| dc.description | Joyce Ho is an assistant professor in Computer Science at Emory University. Her research involves the development of novel data mining and machine learning algorithms to address problems in healthcare. | en_US |
| dc.description | Runtime: 68:42 minutes | en_US |
| dc.description.abstract | Mining biomedical text can be useful for validating new disease subgroups or summarizing information to guide policies and decision making. Yet, existing work predominately focuses on efficient information retrieval. There are other applications where mining biomedical text can be useful. As two motivating examples, researchers are discovering new disease subgroups from secondary analyses of electronic health records. However, such subgroups need to be validated or aligned with current literature. Similarly, systematic reviews serve as a mechanism to summarize current evidence related to a research question. In both scenarios, the abundance of articles can be overwhelming to process manually. In this talk, I will first introduce a scalable framework that produces evidence sets (or relevant articles) using a large corpus of online medical literature. I will discuss some of the challenges associated with term representation and mining biomedical text. I will then present recent work on automating the screening process to allow health services researchers to more efficiently summarize the current findings. | en_US |
| dc.format.extent | 68:42 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/62481 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | CHAI Seminar Series | en_US |
| dc.subject | Biomedical literature | en_US |
| dc.subject | Data mining | en_US |
| dc.title | Automation of Evidence Matching and Systemic Reviews Using Web-Based Medical Literature | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Center for Health Analytics and Informatics | |
| local.relation.ispartofseries | CHAI Seminar Series | |
| relation.isOrgUnitOfPublication | b83ddeb7-f683-4ceb-8ebc-2a41317d413b | |
| relation.isSeriesOfPublication | 3b5571e0-1442-48f6-946c-da5f3b2c4885 |
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