Paper Info
Title
Effects of Plasma Transfusion on Perioperative Bleeding Complications: A Machine Learning Approach.
Abstract
Perioperative bleeding (PB) is associated with increased patient morbidity and mortality, and results in substantial health care resource utilization. To assess bleeding risk, a routine practice in most centers is to use indicators such as elevated values of the International Normalized Ratio (INR). For patients with elevated INR, the routine therapy option is plasma transfusion. However, the predictive accuracy of INR and the value of plasma transfusion still remains unclear. Accurate methods are therefore needed to identifi early the patients with increased risk of bleeding. The goal of this work is to apply advanced machine learning methods to study the relationship between preoperative plasma transfusion (PPT) and PB in patients with elevated INR undergoing noncardiac surgery. The problem is cast under the framework of causal inference where robust meaningful measures to quantify thethe effect of PPT on PB are estimated. Results show that both machine learning and standard statistical methods generally agree that PPT negatively impacts PB and other important patient outcomes. However, machine learning methods show significant results, and machine learning boosting methods are found to make less errors in predicting PB.
Year
DOI
Venue
2015
10.3233/978-1-61499-564-7-721
Studies in Health Technology and Informatics
Keywords
Field
DocType
Blood Transfusion,Bleeding,Treatment Effect,Classification,Machine Learning,Electronic Health Records
Causal inference,Data mining,Text mining,Blood transfusion,Perioperative,Artificial intelligence,Treatment effect,Medicine,Machine learning
Conference
Volume
ISSN
Citations 
216
0926-9630
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Che Ngufor1179.73
Dennis Murphree230.73
Sudhindra Upadhyaya351.98
Nageswar Madde400.68
Daryl J Kor554.01
Jyotishman Pathak667776.52