麻豆中文字幕丨欧美一级免费在线观看丨国产成人无码av在线播放无广告丨国产第一毛片丨国产视频观看丨七妺福利精品导航大全丨国产亚洲精品自在久久vr丨国产成人在线看丨国产超碰人人模人人爽人人喊丨欧美色图激情小说丨欧美中文字幕在线播放丨老少交欧美另类丨色香蕉在线丨美女大黄网站丨蜜臀av性久久久久蜜臀aⅴ麻豆丨欧美亚洲国产精品久久蜜芽直播丨久久99日韩国产精品久久99丨亚洲黄色免费看丨极品少妇xxx丨国产美女极度色诱视频www

Chinese researchers use AI to explore diabetes classification

Source: Xinhua| 2019-01-10 19:05:25|Editor: Xiang Bo
Video PlayerClose

BEIJING, Jan. 10 (Xinhua) -- Chinese researchers are using artificial intelligence (AI) to classify different types of diabetes, which may help Chinese patients obtain more precise treatment.

Different types of diabetes require diverse treatment. The current diabetes classification system, which has been used for more than 20 years is based on cause and pathological?features, which has limitations in guiding clinical treatment.

Researchers from Peking University People's Hospital are working on a more elaborate classification of diabetes that may support individualized treatment.

They conducted research on diabetes classification based on the data of 2,316 Chinese people newly diagnosed with diabetes and 815 Americans.

Using the AI clustering method, they separate the two groups into four diabete subtypes based on five variables including age, BMI, blood glucose levels and insulin resistance indexes.

According to Zou Xiantong, one of the researchers, a previous study from Northern Europe has used similar methods to divide diabetes into five subgroups and demonstrated that the subgroups have different clinical manifestations and corresponding treatments. However, all cases involved in the study were from Northern Europe, and it is unknown whether it is applicable to other populations.

"We hope our research may provide data support for more accurate typing and treatment of diabetes in the Chinese population," Zou said.

The data analysis showed that the main clinical features of the four subtypes were basically consistent in the Chinese and U.S. groups, which also coincided with the subtype characteristics of the Northern Europe research.

The research was published in the journal The Lancet Diabetes and Endocrinology.

TOP STORIES
EDITOR’S CHOICE
MOST VIEWED
EXPLORE XINHUANET
010020070750000000000000011100001377340951