Applications of data mining to postoperative pain management

Yuh-Jyh Hu; Rong Hong Jan; Kuo-Chen Wang; Yu-Chee Tseng; Tien Hsiung Ku; Shu Fen Yang

Applications of data mining to postoperative pain management

Yuh-Jyh Hu^*, Rong Hong Jan, Kuo-Chen Wang, Yu-Chee Tseng, Tien Hsiung Ku, Shu Fen Yang

^*Corresponding author for this work

Department of Computer Science

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

Abstract

Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. Undertreatment of pain may impede short-term recovery, and may even have a detrimental long-tern effect on health. Despite the advancement in postoperative pain management, pain relief and patient satisfaction still does not meet some patients' requirement. By applying data mining techniques, this study aimed to identify the predictive factors for anesthetic dosage and PCA (Patient Controlled Analgesia) demands. With the assistance of Changhua Christian Hospital, we collected 1655 PCA patient records. We analyzed patient PCA usage profiles. We concentrated on two prediction tasks in this study: (a) postoperative analgesic consumption, and (2) PCA setting readjustment.

Original language	English
Title of host publication	Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
Pages	3-10
Number of pages	8
State	Published - 1 Dec 2011
Event	IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011 - Rome, Italy Duration: 24 Jul 2011 → 26 Jul 2011

Publication series

Name	Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011

Conference

Conference	IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
Country/Territory	Italy
City	Rome
Period	24/07/11 → 26/07/11

Keywords

Bagging
Clustering
Decision trees
Intramuscular (IM)
Patient controlled analgesia (PCA)

Cite this

Hu, Y.-J., Jan, R. H., Wang, K.-C., Tseng, Y.-C., Ku, T. H., & Yang, S. F. (2011). Applications of data mining to postoperative pain management. In Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011 (pp. 3-10). (Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011).

Hu, Yuh-Jyh ; Jan, Rong Hong ; Wang, Kuo-Chen et al. / Applications of data mining to postoperative pain management. Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011. 2011. pp. 3-10 (Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011).

@inproceedings{4b02b8142d7c4177819ef0d55df14236,

title = "Applications of data mining to postoperative pain management",

abstract = "Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. Undertreatment of pain may impede short-term recovery, and may even have a detrimental long-tern effect on health. Despite the advancement in postoperative pain management, pain relief and patient satisfaction still does not meet some patients' requirement. By applying data mining techniques, this study aimed to identify the predictive factors for anesthetic dosage and PCA (Patient Controlled Analgesia) demands. With the assistance of Changhua Christian Hospital, we collected 1655 PCA patient records. We analyzed patient PCA usage profiles. We concentrated on two prediction tasks in this study: (a) postoperative analgesic consumption, and (2) PCA setting readjustment.",

keywords = "Bagging, Clustering, Decision trees, Intramuscular (IM), Patient controlled analgesia (PCA)",

author = "Yuh-Jyh Hu and Jan, {Rong Hong} and Kuo-Chen Wang and Yu-Chee Tseng and Ku, {Tien Hsiung} and Yang, {Shu Fen}",

year = "2011",

month = dec,

day = "1",

language = "English",

isbn = "9789728939533",

series = "Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011",

pages = "3--10",

booktitle = "Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011",

note = "IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011 ; Conference date: 24-07-2011 Through 26-07-2011",

}

Hu, Y-J, Jan, RH, Wang, K-C, Tseng, Y-C, Ku, TH & Yang, SF 2011, Applications of data mining to postoperative pain management. in Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011. Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011, pp. 3-10, IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011, Rome, Italy, 24/07/11.

Applications of data mining to postoperative pain management. / Hu, Yuh-Jyh; Jan, Rong Hong; Wang, Kuo-Chen et al.
Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011. 2011. p. 3-10 (Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011).

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

TY - GEN

T1 - Applications of data mining to postoperative pain management

AU - Hu, Yuh-Jyh

AU - Jan, Rong Hong

AU - Wang, Kuo-Chen

AU - Tseng, Yu-Chee

AU - Ku, Tien Hsiung

AU - Yang, Shu Fen

PY - 2011/12/1

Y1 - 2011/12/1

N2 - Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. Undertreatment of pain may impede short-term recovery, and may even have a detrimental long-tern effect on health. Despite the advancement in postoperative pain management, pain relief and patient satisfaction still does not meet some patients' requirement. By applying data mining techniques, this study aimed to identify the predictive factors for anesthetic dosage and PCA (Patient Controlled Analgesia) demands. With the assistance of Changhua Christian Hospital, we collected 1655 PCA patient records. We analyzed patient PCA usage profiles. We concentrated on two prediction tasks in this study: (a) postoperative analgesic consumption, and (2) PCA setting readjustment.

AB - Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. Undertreatment of pain may impede short-term recovery, and may even have a detrimental long-tern effect on health. Despite the advancement in postoperative pain management, pain relief and patient satisfaction still does not meet some patients' requirement. By applying data mining techniques, this study aimed to identify the predictive factors for anesthetic dosage and PCA (Patient Controlled Analgesia) demands. With the assistance of Changhua Christian Hospital, we collected 1655 PCA patient records. We analyzed patient PCA usage profiles. We concentrated on two prediction tasks in this study: (a) postoperative analgesic consumption, and (2) PCA setting readjustment.

KW - Bagging

KW - Clustering

KW - Decision trees

KW - Intramuscular (IM)

KW - Patient controlled analgesia (PCA)

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M3 - Conference contribution

AN - SCOPUS:84865093576

SN - 9789728939533

T3 - Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011

SP - 3

EP - 10

BT - Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011

T2 - IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011

Y2 - 24 July 2011 through 26 July 2011

ER -

Hu YJ, Jan RH, Wang KC, Tseng YC, Ku TH, Yang SF. Applications of data mining to postoperative pain management. In Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011. 2011. p. 3-10. (Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011).

Applications of data mining to postoperative pain management

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Keywords

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