Featured Research

from universities, journals, and other organizations

Machine learning technique designed to improve consumer medical searches

Date:
November 18, 2010
Source:
Georgia Institute of Technology
Summary:
Researchers have created a machine-learning model that enables the medical sites to "learn" dialect and other medical vernacular, thereby improving their performance for users who use such language themselves.

Medical websites like WebMD provide consumers with more access than ever before to health and medical information, but the sites' utility becomes limited if users use unclear or unorthodox language to describe conditions in a site search. However, a group of Georgia Tech researchers have created a machine-learning model that enables the sites to "learn" dialect and other medical vernacular, thereby improving their performance for users who use such language themselves.

Related Articles


Called "diaTM" (short for "dialect topic modeling"), the system learns by comparing multiple medical documents written in different levels of technical language. By comparing enough of these documents, diaTM eventually learns which medical conditions, symptoms and procedures are associated with certain dialectal words or phrases, thus shrinking the "language gap" between consumers with health questions and the medical databases they turn to for answers.

"The language gap problem seems to be the most acute in the medical domain," said Hongyuan Zha, professor in the School of Computational Science & Engineering and a paper co-author. "Providing a solution for this domain will have a high impact on maintaining and improving people's health."

To educate diaTM in various modes of medical language, Crain and his fellow researchers pulled publicly available documents not only from WebMD but also Yahoo! Answers, PubMed Central, the Centers for Disease Control & Prevention website, and other sources. After processing enough documents, he said, diaTM can learn that the word "gunk," for example, is often a vernacular term for "discharge," and it can process user searches that incorporate the word "gunk" appropriately.

In this initial study using small-scale experiments, the researchers found that diaTM can achieve a 25 percent improvement in nDCG ("normalized discounted cumulative gain"), a scientific term that refers to the relevance of information retrieval in a web search. Zha, whose research focuses on Internet search engines and their related algorithms, said a 5 percent improvement in nDCG is "very significant."

"DiaTM figures out enough language relationships that over time it does quite well," said Steven Crain, Ph.D. student in computer science and lead author of the paper that describes diaTM. "Another benefit is we're not doing word-for-word equivalencies, so 'gunk' doesn't necessarily have to be connected to 'discharge,' as long as it's recognized that 'gunk' is related to infections."

Also, diaTM is not limited to medical search; it is a machine-learning technique that would work equally well in any topic-related search. In addition to approaching websites about incorporating diaTM into their search engines, Crain said one next stop is to develop the model so that it can learn dialects by looking at patterns that do not make sense from a topical perspective. For example, using a similar algorithm he was able to automatically discover dialects including text-speak dialect (e.g. "b4" as a subsititue for "before"), but the dialects were mixed in with topically-related groups of words.

"We're trying to get to where you can isolate just the dialects," Crain said.

"This feature will help common users of medical websites," Zha said. "It will help enable consumers with a relatively low level of health literacy to access the critical medical information they need."

DiaTM is described in the paper, "Dialect Topic Modeling for Improved Consumer Medical Search," to be presented by Crain at the American Medical Informatics Association Annual Symposium, Nov. 17 in Washington, D.C. Crain's coauthors include Hongyuan Zha, professor in the School of Computational Science & Engineering; Shuang-Hong Yang, a Ph.D. student in Computational Science and Engineering; and Yu Jiao, research scientist at Oak Ridge National Laboratory (ORNL). The research was conducted with partial funding from ORNL, Microsoft and Hewlett-Packard.


Story Source:

The above story is based on materials provided by Georgia Institute of Technology. Note: Materials may be edited for content and length.


Cite This Page:

Georgia Institute of Technology. "Machine learning technique designed to improve consumer medical searches." ScienceDaily. ScienceDaily, 18 November 2010. <www.sciencedaily.com/releases/2010/11/101117104522.htm>.
Georgia Institute of Technology. (2010, November 18). Machine learning technique designed to improve consumer medical searches. ScienceDaily. Retrieved January 29, 2015 from www.sciencedaily.com/releases/2010/11/101117104522.htm
Georgia Institute of Technology. "Machine learning technique designed to improve consumer medical searches." ScienceDaily. www.sciencedaily.com/releases/2010/11/101117104522.htm (accessed January 29, 2015).

Share This


More From ScienceDaily



More Health & Medicine News

Thursday, January 29, 2015

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Malnutrition on the Rise as Violence Flares in C. Africa

Malnutrition on the Rise as Violence Flares in C. Africa

AFP (Jan. 28, 2015) Violence can flare up at any moment in Bambari with only a bridge separating Muslims and Christians. Malnutrition is on the rise and lack of water means simple cooking fires threaten to destroy makeshift camps where people are living. Duration: 00:40 Video provided by AFP
Powered by NewsLook.com
Poultry Culled in Taiwan to Thwart Bird Flu

Poultry Culled in Taiwan to Thwart Bird Flu

Reuters - News Video Online (Jan. 28, 2015) Taiwan culls over a million poultry in efforts to halt various strains of avian flu. Julie Noce reports. Video provided by Reuters
Powered by NewsLook.com
Media Criticizing Parents For Not Vaccinating Children

Media Criticizing Parents For Not Vaccinating Children

Newsy (Jan. 28, 2015) As the Disneyland measles outbreak continues to spread, the media says parents who choose not to vaccinate their children are part of the cause. Video provided by Newsy
Powered by NewsLook.com
Shark Bite Victim Making Amazing Recovery

Shark Bite Victim Making Amazing Recovery

AP (Jan. 27, 2015) A Texas woman who lost more than five pounds of flesh to a shark in the Bahamas earlier this month could be released from a Florida hospital soon. Experts believe she was bitten by a bull shark while snorkeling. (Jan. 27) Video provided by AP
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:

Breaking News:

Strange & Offbeat Stories


Health & Medicine

Mind & Brain

Living & Well

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile: iPhone Android Web
Follow: Facebook Twitter Google+
Subscribe: RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins