Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia

Kee Siong Ng; Y. Shan; D.W. Murray; A. Sutinen; B. Scharwz; D. Jeacocke; J. Farrugia

Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia

Kee Siong Ng, Y. Shan, D.W. Murray, A. Sutinen, B. Scharwz, D. Jeacocke, J. Farrugia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework that brings together disparate data mining techniques is adopted. Several generally applicable techniques for extracting features from spatial and temporal data are also discussed. The system was evaluated with input from domain experts and was found to achieve high hit rates. We also discuss some lessons drawn from the experience.

Original language	English
Title of host publication	IEEE International Conference on Data Mining (ICDM 2010) proceedings
Editors	Conference Program Committee
Place of Publication	Piscataway USA
Publisher	IEEE Computer Society
Edition	Peer Reviewed
ISBN (Print)	9780769542560
Publication status	Published - 2010
Event	IEEE International Conference on Data Mining (ICDM 2010) - Sydney Australia, Australia Duration: 1 Jan 2010 → …

Conference

Conference	IEEE International Conference on Data Mining (ICDM 2010)
Country/Territory	Australia
Period	1/01/10 → …
Other	December 14-17 2010

Cite this

@inproceedings{ce39ef94c27b41beab9e12e6f6541934,

title = "Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia",

abstract = "This paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework that brings together disparate data mining techniques is adopted. Several generally applicable techniques for extracting features from spatial and temporal data are also discussed. The system was evaluated with input from domain experts and was found to achieve high hit rates. We also discuss some lessons drawn from the experience.",

author = "Ng, {Kee Siong} and Y. Shan and D.W. Murray and A. Sutinen and B. Scharwz and D. Jeacocke and J. Farrugia",

year = "2010",

language = "English",

isbn = "9780769542560",

editor = "{Conference Program Committee}",

booktitle = "IEEE International Conference on Data Mining (ICDM 2010) proceedings",

publisher = "IEEE Computer Society",

edition = "Peer Reviewed",

note = "IEEE International Conference on Data Mining (ICDM 2010) ; Conference date: 01-01-2010",

}

Ng, KS, Shan, Y, Murray, DW, Sutinen, A, Scharwz, B, Jeacocke, D & Farrugia, J 2010, Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia. in CPC (ed.), IEEE International Conference on Data Mining (ICDM 2010) proceedings. Peer Reviewed edn, IEEE Computer Society, Piscataway USA, IEEE International Conference on Data Mining (ICDM 2010), Australia, 1/01/10.

Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia. / Ng, Kee Siong; Shan, Y.; Murray, D.W. et al.
IEEE International Conference on Data Mining (ICDM 2010) proceedings. ed. / Conference Program Committee. Peer Reviewed. ed. Piscataway USA: IEEE Computer Society, 2010.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia

AU - Ng, Kee Siong

AU - Shan, Y.

AU - Murray, D.W.

AU - Sutinen, A.

AU - Scharwz, B.

AU - Jeacocke, D.

AU - Farrugia, J.

PY - 2010

Y1 - 2010

N2 - This paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework that brings together disparate data mining techniques is adopted. Several generally applicable techniques for extracting features from spatial and temporal data are also discussed. The system was evaluated with input from domain experts and was found to achieve high hit rates. We also discuss some lessons drawn from the experience.

AB - This paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework that brings together disparate data mining techniques is adopted. Several generally applicable techniques for extracting features from spatial and temporal data are also discussed. The system was evaluated with input from domain experts and was found to achieve high hit rates. We also discuss some lessons drawn from the experience.

M3 - Conference contribution

SN - 9780769542560

BT - IEEE International Conference on Data Mining (ICDM 2010) proceedings

A2 - null, Conference Program Committee

PB - IEEE Computer Society

CY - Piscataway USA

T2 - IEEE International Conference on Data Mining (ICDM 2010)

Y2 - 1 January 2010

ER -

Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia

Abstract

Conference

Fingerprint

Cite this