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Appl. Sci. 2018, 8(9), 1559; https://doi.org/10.3390/app8091559

Prediction of Preoperative Blood Preparation for Orthopedic Surgery Patients: A Supervised Learning Approach

1,†
,
2,3,†
,
4,5
,
4
and
4,5,6,7,*
1
Department of Laboratory Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City 600, Taiwan
2
Department of Obstetrics and Gynecology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei 231, Taiwan
3
School of Medicine, Tzu Chi University, Hualien 970, Taiwan
4
Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County 621, Taiwan
5
Center for Innovative Research on Aging Society, National Chung Cheng University, Chiayi 621, Taiwan
6
Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chiayi 600, Taiwan
7
School of Medicine, National Yang-Ming University, Taipei 112, Taiwan
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 9 July 2018 / Revised: 30 August 2018 / Accepted: 31 August 2018 / Published: 5 September 2018
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
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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. View Full-Text
Keywords: blood transfusion prediction; data mining; supervised learning techniques; orthopedic surgery; feature selection blood transfusion prediction; data mining; supervised learning techniques; orthopedic surgery; feature selection
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Chang, C.-M.; Hung, J.-H.; Hu, Y.-H.; Lee, P.-J.; Shen, C.-C. Prediction of Preoperative Blood Preparation for Orthopedic Surgery Patients: A Supervised Learning Approach. Appl. Sci. 2018, 8, 1559.

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