Proposal of medical KDD support user interface utilizing rule interestingness measures

Miho Ohsaki, Hidenao Abe, Shusaku Tsumoto, Hideto Yokoi, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper discusses the utilization of rule interestingness measures in medical KDD. We selected various interestingness measures and conducted experiments using clinical datasets to examine how they can estimate real human interest. The results indicate that some of them have a stable, reasonable estimation performance and the combinational use of interestingness measures will contribute to medical KDD. We then developed a prototype of medical KDD support user interface based on the experimental outcomes. We conducted a case study in which a medical expert tried to discover medical knowledge with the prototype. Some interesting rules were actually obtained and that indicates the potential of the user interface.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages759-764
Number of pages6
Publication statusPublished - 2006
Event6th IEEE International Conference on Data Mining - Workshops, ICDM 2006 - Hong Kong, China
Duration: 2006 Dec 182006 Dec 18

Other

Other6th IEEE International Conference on Data Mining - Workshops, ICDM 2006
CountryChina
CityHong Kong
Period06/12/1806/12/18

Fingerprint

User interfaces
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ohsaki, M., Abe, H., Tsumoto, S., Yokoi, H., & Yamaguchi, T. (2006). Proposal of medical KDD support user interface utilizing rule interestingness measures. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 759-764). [4063727]

Proposal of medical KDD support user interface utilizing rule interestingness measures. / Ohsaki, Miho; Abe, Hidenao; Tsumoto, Shusaku; Yokoi, Hideto; Yamaguchi, Takahira.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 759-764 4063727.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ohsaki, M, Abe, H, Tsumoto, S, Yokoi, H & Yamaguchi, T 2006, Proposal of medical KDD support user interface utilizing rule interestingness measures. in Proceedings - IEEE International Conference on Data Mining, ICDM., 4063727, pp. 759-764, 6th IEEE International Conference on Data Mining - Workshops, ICDM 2006, Hong Kong, China, 06/12/18.
Ohsaki M, Abe H, Tsumoto S, Yokoi H, Yamaguchi T. Proposal of medical KDD support user interface utilizing rule interestingness measures. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 759-764. 4063727
Ohsaki, Miho ; Abe, Hidenao ; Tsumoto, Shusaku ; Yokoi, Hideto ; Yamaguchi, Takahira. / Proposal of medical KDD support user interface utilizing rule interestingness measures. Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. pp. 759-764
@inproceedings{da6ba8462576406bb12e223065985612,
title = "Proposal of medical KDD support user interface utilizing rule interestingness measures",
abstract = "This paper discusses the utilization of rule interestingness measures in medical KDD. We selected various interestingness measures and conducted experiments using clinical datasets to examine how they can estimate real human interest. The results indicate that some of them have a stable, reasonable estimation performance and the combinational use of interestingness measures will contribute to medical KDD. We then developed a prototype of medical KDD support user interface based on the experimental outcomes. We conducted a case study in which a medical expert tried to discover medical knowledge with the prototype. Some interesting rules were actually obtained and that indicates the potential of the user interface.",
author = "Miho Ohsaki and Hidenao Abe and Shusaku Tsumoto and Hideto Yokoi and Takahira Yamaguchi",
year = "2006",
language = "English",
isbn = "0769527027",
pages = "759--764",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",

}

TY - GEN

T1 - Proposal of medical KDD support user interface utilizing rule interestingness measures

AU - Ohsaki, Miho

AU - Abe, Hidenao

AU - Tsumoto, Shusaku

AU - Yokoi, Hideto

AU - Yamaguchi, Takahira

PY - 2006

Y1 - 2006

N2 - This paper discusses the utilization of rule interestingness measures in medical KDD. We selected various interestingness measures and conducted experiments using clinical datasets to examine how they can estimate real human interest. The results indicate that some of them have a stable, reasonable estimation performance and the combinational use of interestingness measures will contribute to medical KDD. We then developed a prototype of medical KDD support user interface based on the experimental outcomes. We conducted a case study in which a medical expert tried to discover medical knowledge with the prototype. Some interesting rules were actually obtained and that indicates the potential of the user interface.

AB - This paper discusses the utilization of rule interestingness measures in medical KDD. We selected various interestingness measures and conducted experiments using clinical datasets to examine how they can estimate real human interest. The results indicate that some of them have a stable, reasonable estimation performance and the combinational use of interestingness measures will contribute to medical KDD. We then developed a prototype of medical KDD support user interface based on the experimental outcomes. We conducted a case study in which a medical expert tried to discover medical knowledge with the prototype. Some interesting rules were actually obtained and that indicates the potential of the user interface.

UR - http://www.scopus.com/inward/record.url?scp=57349083515&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=57349083515&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:57349083515

SN - 0769527027

SN - 9780769527024

SP - 759

EP - 764

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

ER -