International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 11 | Nov 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Blood Transfusion success rate prediction using Artificial Intelligence Rushabh Jaiswal1 1B.Tech.
Student, Information Technology, Veermata Jijabai Technological Institute, Mumbai, Maharashtra 400019 ---------------------------------------------------------------------***--------------------------------------------------------------------surgery are also poorly documented. Clinical prediction Abstract - Surgery has made extensive use of red blood
models and patients' preoperative risk factors have been used in studies to predict blood transfusions in craniofacial, obstetric, and joint surgery (9–11). In medicine, artificial intelligence (AI) is being used more and more to help with diagnosis, treatment, automatic classification, and rehabilitation. An AI method called the machine learning algorithm is made to mimic human intelligence by finding patterns of reasoning about the data that is available (16). Machine learning algorithms can be used to predict relevant information from basic data, like whether blood transfusions are required. Mitral valve disease patients exhibit high homogeneity, standard diagnostic and treatment procedures, comparable patient data, and a low rate of bleeding during surgery. In order to help the surgeon, determine whether or not a patient requires an intraoperative blood transfusion, we use machine learning models to investigate the risk factors that influence blood transfusion during mitral valve surgery and precisely define these factors' boundaries. Following the STROBE reporting checklist, we present the following article.
cell (RBC) transfusion therapy, which has resulted in excellent treatment outcomes. However, doctors sometimes use their experience to determine whether or not RBC transfusions are necessary. In this study, we use machine learning models to predict whether a patient will require an intraoperative blood transfusion of red blood cells (RBCs) during mitral valve surgery. In surgery, blood transfusions are frequently used. The majority of blood products are used in cardiac surgery, where the rate of blood transfusions ranges from 40% to 90% (1-3). However, this treatment has advantages and disadvantages. In critically ill patients, transfusion has been linked to high rates of morbidity and mortality (4). Blood transfusion has been linked in some recent studies to worse outcomes, such as an increased risk of renal failure and infection and respiratory, circulatory, and neurological complications following cardiac surgery (5,6). Red blood cell (RBC) transfusion is linked to an increased risk of postoperative infections and mortality, according to a review of studies on cardiac surgery (7-9). It is common knowledge that blood products are frequently wasted, which may indicate a lack of evidence-based procedures for blood transfusion (10-12). The writer of this paper has aimed to predict the success rate of the transfusion using the XGBoost technique and gradient boost technique.
2. Literature Overview The study aims to identify the appropriate variables and the AI method for predicting optimal demand and minimizing excess and shortage. It is intended to make predictions by taking into account the variables that will affect the prediction but have not been discussed in the literature. In the literature, variables related to human biologies like age, blood group, and gender make up the inputs of blood demand models. However, the external environment has an impact on the dynamic process of blood component demand. In contrast to other studies, the study takes into account fifteen distinct variables, including temperature, province population, and the number of operations. In the writing, no review has analyzed the outside factors that influence RBCs request. Then, based on the established variables, the AI method that best predicts demand is investigated. Weekly, unit, and average predictions from the determined AI methods were compared. The support vector machine was found to be the most suitable prediction method in light of the comparisons. A subfield of artificial intelligence that uses computational modeling to learn from data is known as machine learning. High-order relationships between
Key Words:
Blood Transfusion, Machine Learning, XGBoost, Gradient Boost, Prediction, Artificial Intelligence
1. INTRODUCTION Before the surgery, it is necessary to provide clinicians with practical guidance for making clinical decisions and to predict RBC transfusion. On a clinical level, a patient's hemoglobin level and anemia symptoms are the primary considerations for doctors when considering a transfusion. However, other perioperative indicators, such as essential patient characteristics like sex, age, and weight, should not be overlooked; preoperative side effects like the presence of entry hypertension, ascites, and hepatic encephalopathy; and preoperative laboratory results like transaminases, creatinine, and hemoglobin. The clinical significance of intraoperative and postoperative risk factors like operation time, intraoperative blood loss, and postoperative laboratory indicators as well as the transfusion of RBCs prior to
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