Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine.
Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabel k-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.
China Astronaut Research and Training Center, Beijing 100094, China.
Yong-Zhi
YZ
Li
Data Center of Traditional Chinese Medicine, China Academy of Chinese Medicine Science, Beijing 100700, China.
Guo-Zheng
GZ
Li
China Astronaut Research and Training Center, Beijing 100094, China.
Jian-Yi
JY
Gao
Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Zhi-Feng
ZF
Zhang
China Astronaut Research and Training Center, Beijing 100094, China.
Quan-Chun
QC
Fan
Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Jia-Tuo
JT
Xu
China Astronaut Research and Training Center, Beijing 100094, China.
Gui-E
GE
Bai
Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Kai-Xian
KX
Chen
China Astronaut Research and Training Center, Beijing 100094, China.
Hong-Zhi
HZ
Shi
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
Sheng
S
Sun
China Astronaut Research and Training Center, Beijing 100094, China.
Yu
Y
Liu
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
Feng-Feng
FF
Shao
China Astronaut Research and Training Center, Beijing 100094, China.
Tao
T
Mi
Shanghai Daosheng Medical Technology Co. Ltd., Shanghai 201203, China.
Xin-Hong
XH
Jia
China Astronaut Research and Training Center, Beijing 100094, China.
Shuang
S
Zhao
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
Jia-Chang
JC
Chen
China Astronaut Research and Training Center, Beijing 100094, China.
Jun-Lian
JL
Liu
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
Yu-Meng
YM
Guo
Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Li Ping
LP
Tu
D000465:Algorithms; D006801:Humans; D008516:Medicine, Chinese Traditional; D008954:Models, Biological; D018531:Spacecraft; D013577:Syndrome