Length of stay in PACU among surgical patients using data mining technique

데이터 마이닝을 활용한 외과수술환자의 회복실 체류시간 분석

  • Received : 2013.05.14
  • Accepted : 2013.07.11
  • Published : 2013.07.31


The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. This study was analyzed by decision making tree model using Clementine C&RT(Classification & Regression Tree, CART) as data mining technique. We utilized this data mining technique to analyze medical record of 1,500 people. Whole data were assorted by length of stay in PACU and divided into 3 groups. The result extracted by C5.0 decision tree method showed that important related factors for lengh of stay in PACU are type of operation, preoperative EKG abnormality, anesthetics, operative duration, age.

본 연구의 목적은 회복실 환자의 평균 체류시간을 알아보고, 체류시간에 미치는 요인들을 파악하여 회복실 체류 시간 예측을 위한 분석을 하기 위함이다. 본 연구의 대상자는 상급 종합병원에 입원한 전신 마취 하에 일반외과 수술을 받은 18세 이상 성인 남녀 환자 중 회복실로 입실한 환자를 1,500명을 대상으로 하였고 이중 1,293건을 분석하였다. 회복실 체류시간에 영향을 미치는 요인으로 32항목을 측정하였다. 평균 회복실 체류시간은 72.02분이었다. 수술주기별 관련요인과 회복실 체류시간의 관계를 살펴본 결과 나이, 수술종류, 수술시간, 진통제사용회수가 유의미한 관계를 나타내었다 회복실 체류시간에 가장 영향을 많이 주는 변수는 수술종류이며 그 다음 EKG 이상여부, 나이, 마취제, 수술시간으로 나타났다. 범주 I(30분~60분)은 2개의 경우, 범주 II(61분~90분)도 2개의 경우, 범주 III(91분~120분)은 4개의 경우로 분석되었다.



Supported by : 한림대학교


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