Prediction of preoperative blood preparation for orthopedic surgery patients: A supervised learning approach

Chia Mei Chang, Jeng Hsiu Hung, Ya Han Hu, Pei Ju Lee, Cheng Che Shen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Blood transfusion is a common and often necessary medical procedure during surgery. However, most physicians rely on their personal clinical experience to determine whether a patient requires a transfusion. This generally involves considering the risk of blood loss during surgery, and the preparation of blood is thus regularly requested before surgery. However, unused blood is a particularly severe problem, especially in orthopedic procedures, which not only increases medical resource wastage but also places a burden on medical personnel. This study collected the records of 1396 patients who received an orthopedic surgery in a regional teaching hospital. Data mining techniques, namely support vector machine, C4.5 decision tree, classification and regression tree, and logistic regression (LGR) were employed to predict whether patients undergoing an orthopedic surgery required an intraoperative blood transfusion. The LGR classifier, which was constructed using the CfsSubsetEval module and GeneticSearch method, exhibited optimal prediction accuracy (area under the curve: 78.7%). This study investigated major variables involved in blood transfusions to provide a clear reference for evaluating the necessity of preparing blood for surgical procedures. Data mining techniques can be used to simplify unnecessary blood preparation procedures, thereby reducing the workload of medical staff and minimizing the wastage of medical resources.

Original languageEnglish
Article number1559
JournalApplied Sciences (Switzerland)
Volume8
Issue number9
DOIs
StatePublished - 5 Sep 2018

Keywords

  • Blood transfusion prediction
  • Data mining
  • Feature selection
  • Orthopedic surgery
  • Supervised learning techniques

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