Abstract
In this paper, we present an integrated time-series data mining environment for medical data mining. Medical time-series data mining is one of key issues to get useful clinical knowledge from medical databases. However, users often face difficulties during such medical time-series data mining process for data preprocessing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as shown in other data mining processes. To get more valuable rules for medical experts from a time-series data mining process, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. After implementing this environment, we have done a case study to mine time-series rules from blood/urine biochemical test database on chronic hepatitis patients. The result shows the availability to find out valuable clinical course rules based on time-series pattern extraction. Furthermore, we compared the difference of time-series pattern extraction methods with objective rule evaluation results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Data Mining, ICDM |
| Pages | 127-132 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States Duration: 2007 Oct 28 → 2007 Oct 31 |
Other
| Other | 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 |
|---|---|
| Country | United States |
| City | Omaha, NE |
| Period | 07/10/28 → 07/10/31 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Developing an integrated time-series data mining environment for medical data mining. / Abe, Hidenao; Yokoi, Hideto; Ohsaki, Miho; Yamaguchi, Takahira.
Proceedings - IEEE International Conference on Data Mining, ICDM. 2007. p. 127-132 4476657.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Developing an integrated time-series data mining environment for medical data mining
AU - Abe, Hidenao
AU - Yokoi, Hideto
AU - Ohsaki, Miho
AU - Yamaguchi, Takahira
PY - 2007
Y1 - 2007
N2 - In this paper, we present an integrated time-series data mining environment for medical data mining. Medical time-series data mining is one of key issues to get useful clinical knowledge from medical databases. However, users often face difficulties during such medical time-series data mining process for data preprocessing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as shown in other data mining processes. To get more valuable rules for medical experts from a time-series data mining process, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. After implementing this environment, we have done a case study to mine time-series rules from blood/urine biochemical test database on chronic hepatitis patients. The result shows the availability to find out valuable clinical course rules based on time-series pattern extraction. Furthermore, we compared the difference of time-series pattern extraction methods with objective rule evaluation results.
AB - In this paper, we present an integrated time-series data mining environment for medical data mining. Medical time-series data mining is one of key issues to get useful clinical knowledge from medical databases. However, users often face difficulties during such medical time-series data mining process for data preprocessing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as shown in other data mining processes. To get more valuable rules for medical experts from a time-series data mining process, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. After implementing this environment, we have done a case study to mine time-series rules from blood/urine biochemical test database on chronic hepatitis patients. The result shows the availability to find out valuable clinical course rules based on time-series pattern extraction. Furthermore, we compared the difference of time-series pattern extraction methods with objective rule evaluation results.
UR - http://www.scopus.com/inward/record.url?scp=49549113794&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549113794&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2007.47
DO - 10.1109/ICDMW.2007.47
M3 - Conference contribution
AN - SCOPUS:49549113794
SN - 0769530192
SN - 9780769530192
SP - 127
EP - 132
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
ER -