Characterizing postoperative pain management data by cluster analysis

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

*Corresponding author for this work

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

Abstract

PCA (Patient Controlled Analgesia) is a delivery system for pain medication that makes effective and flexible pain treatments possible by allowing patients to adjust the dosage of analgesics themselves. Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We applied clustering methods to disclose demand patterns among patients over the first 24h of analgesic medication after surgery. We first identified three demand patterns from patient controlled analgesia request log files. We then considered demographic, biomedical, and surgery-related data to evaluate the influence of demand pattern on analgesic requirements. We recovered several associations that concurred with previous findings, and discovered several new correlations.

Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012
Pages438-444
Number of pages7
StatePublished - 1 Dec 2012
Event2012 International Conference on Artificial Intelligence, ICAI 2012 - Las Vegas, NV, United States
Duration: 16 Jul 201219 Jul 2012

Publication series

NameProceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012
Volume1

Conference

Conference2012 International Conference on Artificial Intelligence, ICAI 2012
CountryUnited States
CityLas Vegas, NV
Period16/07/1219/07/12

Keywords

  • Clustering
  • PCA demand
  • Patient controlled analgesia
  • Postoperative pain

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