使用不平衡登记数据的心衰患者院内死亡率预测模型：机器学习方法

IF 1.4 4区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Scientia Iranica Pub Date : 2023-07-18 DOI:10.24200/sci.2023.61637.7412

H. Sabahi, M. Vali, D. Shafie

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引用次数: 0

摘要

9 心力衰竭（HF）是一种心脏功能障碍疾病，死亡率很高，而死亡率大多是通过登记数据 10 计算得出的。这项研究的目的是利用高血压住院病人入院前的登记数据预测他们的院内死亡率。这些数据包括从波斯心血管疾病登记处（Persian Registry Of cardio Vascular diseasE，PROVE）/HF 登记处提取的 3968 条 HF 记录。我们提出了一种包含不平衡集合概率模型 13 的方法，利用登记数据预测住院期间死亡的高血压患者和存活的患者。该模型

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In-hospital mortality prediction model of heart failure patients using imbalanced registry data: A machine learning approach

9 Heart failure (HF) is a cardiac dysfunction disease with a high mortality rate that is mostly calculated via registry 10 data. The objective of this work was to predict in-hospital mortality in patients hospitalized with HF utilizing their 11 before-hospitalization registry data. The data include 3968 HF records extracted from Persian Registry Of cardio 12 Vascular diseasE (PROVE)/HF registry. We proposed a method that contains an imbalanced ensemble probabilistic 13 model which using registry data predicts HF patients who die during hospitalization from those who survive. The

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来源期刊

Scientia Iranica 工程技术-工程：综合

CiteScore

2.90

自引率

7.10%

发文量

审稿时长

2 months

期刊介绍： The objectives of Scientia Iranica are two-fold. The first is to provide a forum for the presentation of original works by scientists and engineers from around the world. The second is to open an effective channel to enhance the level of communication between scientists and engineers and the exchange of state-of-the-art research and ideas. The scope of the journal is broad and multidisciplinary in technical sciences and engineering. It encompasses theoretical and experimental research. Specific areas include but not limited to chemistry, chemical engineering, civil engineering, control and computer engineering, electrical engineering, material, manufacturing and industrial management, mathematics, mechanical engineering, nuclear engineering, petroleum engineering, physics, nanotechnology.