Name
Abstract | ||
|---|---|---|
It is a still a world-wide challenge that hypertensive patients maintain a satisfactory blood pressure control. In TCM practice, traditional physical therapy has shown beneficial to blood pressure (BP) controlling. As the amount of bio medical data in leading databases (i.e. SinoMed, etc.) is growing at an exponential rate, it might be possible to get something meaningful through the techniques developed in data mining. In this paper, focused on hypertension, we proposed an algorithm named two dimensions data slicing to mine rules of Chinese medicinal physical therapies (massage, cupping and so on). The process of mining was done in two dimensions. The one-dimension analyzes the frequencies. The two-dimension analyzes the frequencies of co-existed keyword pairs. By examining the results of these two dimensions, although some noises existed, most regular knowledge of this disease is mined out. This algorithm might be useful in mining rules in the literature of traditional Chinese medicine. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBM.2013.6732636 | BIBM |
Keywords | Field | DocType |
traditional chinese medicine,tcm practice,diseases,hypertension treatment,biomedical data,hypertension,blood pressure control,physical therapy,two-dimension data slicing algorithm,traditional physical therapy,hypertensive patients,chinese medicinal physical therapy,disease,data mining,medical computing,text analysis,text mining,patient treatment,blood | Text mining,Computer science,Traditional Chinese medicine,Medical treatment,Artificial intelligence,Machine learning,Blood pressure control,Patient treatment | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 2 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongmei Zhou | 1 | 0 | 1.35 |
| Jinrui Guo | 2 | 0 | 1.01 |
| Xiaoxia Ren | 3 | 165 | 8.37 |
| Rongfen Dong | 4 | 0 | 1.35 |
| Jinrong Zhang | 5 | 0 | 1.35 |
| Zhaoli Cui | 6 | 0 | 1.01 |
| Na Ge | 7 | 0 | 0.68 |
| Yong Tan | 8 | 0 | 0.68 |
| Aiping Lu | 9 | 0 | 0.68 |
| Miao Jiang | 10 | 63 | 12.07 |
| Yahong Wang | 11 | 0 | 1.01 |
| Yaoxian Wang | 12 | 0 | 2.70 |
| Guang Zheng | 13 | 28 | 10.72 |
| Hongtao Guo | 14 | 29 | 9.71 |