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
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U2 - 10.1109/ICDMW.2007.47
DO - 10.1109/ICDMW.2007.47
M3 - Conference contribution
AN - SCOPUS:49549113794
SN - 0769530192
SN - 9780769530192
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 127
EP - 132
BT - ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
T2 - 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Y2 - 28 October 2007 through 31 October 2007
ER -