Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.

Md Shahid Ansari, Dinesh Jain, Sandeep Budhiraja
{"title":"Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.","authors":"Md Shahid Ansari, Dinesh Jain, Sandeep Budhiraja","doi":"10.1016/j.htct.2023.09.2365","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×10<sup>9</sup>/L or less. This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. The objective was to study the risk factors that can help to predict blood component transfusion needs.</p><p><strong>Methods: </strong>Eight predictive models were developed based on retrospective data from a private group of hospitals in India. A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the \"XGBoost\" model.</p><p><strong>Results: </strong>Sixteen vital variables were finally selected as having the most significant effects on blood component transfusion prediction. The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699-0.795) than the other models.</p><p><strong>Conclusion: </strong>Predictive modelling techniques can be utilized to streamline blood component preparation procedures and can help in the triage of high-risk patients and readiness of caregivers to provide blood component transfusions when required. This study demonstrates the potential of multilayer algorithms to reasonably predict any blood component transfusion needs which may help healthcare providers make more informed decisions regarding patient care.</p>","PeriodicalId":94026,"journal":{"name":"Hematology, transfusion and cell therapy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hematology, transfusion and cell therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.htct.2023.09.2365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×109/L or less. This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. The objective was to study the risk factors that can help to predict blood component transfusion needs.

Methods: Eight predictive models were developed based on retrospective data from a private group of hospitals in India. A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the "XGBoost" model.

Results: Sixteen vital variables were finally selected as having the most significant effects on blood component transfusion prediction. The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699-0.795) than the other models.

Conclusion: Predictive modelling techniques can be utilized to streamline blood component preparation procedures and can help in the triage of high-risk patients and readiness of caregivers to provide blood component transfusions when required. This study demonstrates the potential of multilayer algorithms to reasonably predict any blood component transfusion needs which may help healthcare providers make more informed decisions regarding patient care.

住院登革热患者任何血液成分输血的机器学习预测模型。
背景:在登革热流行期间,输血是一种常见且经常必要的医疗实践。当患者出现严重登革热或血小板减少10×109/L或以下时,需要输血。因此,本研究调查了八种不同机器学习算法的风险因素、性能和有效性,以预测住院确诊登革热病例的血液成分输血需求。目的是研究有助于预测血液成分输血需求的危险因素。方法:基于印度一组私立医院的回顾性数据,建立了8个预测模型。python包SHAP (SHapley Additive exPlanations)用于解释“XGBoost”模型的输出。结果:最终筛选出16个对血液成分输血预测影响最显著的重要变量。XGBoost模型的预测性能明显更好(曲线下面积:0.793;95%置信区间:0.699-0.795)。结论:预测建模技术可用于简化血液成分制备程序,有助于高危患者的分诊,并有助于护理人员在需要时提供血液成分输血。本研究证明了多层算法在合理预测任何血液成分输血需求方面的潜力,这可能有助于医疗保健提供者在患者护理方面做出更明智的决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信