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 November 26, 2014 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 November 26, 2014).

Share This


More From ScienceDaily



More Health & Medicine News

Wednesday, November 26, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Pet Dogs to Be Used in Anti-Ageing Trial

Pet Dogs to Be Used in Anti-Ageing Trial

Reuters - Innovations Video Online (Nov. 26, 2014) Researchers in the United States are preparing to discover whether a drug commonly used in human organ transplants can extend the lifespan and health quality of pet dogs. Video provided by Reuters
Powered by NewsLook.com
From Popcorn To Vending Snacks: FDA Ups Calorie Count Rules

From Popcorn To Vending Snacks: FDA Ups Calorie Count Rules

Newsy (Nov. 25, 2014) The US FDA is announcing new calorie rules on Tuesday that will require everywhere from theaters to vending machines to include calorie counts. Video provided by Newsy
Powered by NewsLook.com
Daily Serving Of Yogurt Could Reduce Risk Of Type 2 Diabetes

Daily Serving Of Yogurt Could Reduce Risk Of Type 2 Diabetes

Newsy (Nov. 25, 2014) Need another reason to eat yogurt every day? Researchers now say it could reduce a person's risk of developing type 2 diabetes. Video provided by Newsy
Powered by NewsLook.com
Madagascar Working to Contain Plague Outbreak

Madagascar Working to Contain Plague Outbreak

AFP (Nov. 24, 2014) Madagascar said Monday it is trying to contain an outbreak of plague -- similar to the Black Death that swept Medieval Europe -- that has killed 40 people and is spreading to the capital Antananarivo. Duration: 00:42 Video provided by AFP
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