Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods.

IF 1.8 Q4 ENDOCRINOLOGY & METABOLISM

Journal of Diabetes and Metabolic Disorders Pub Date : 2023-05-13 DOI:10.1007/s40200-023-01228-y

Pooneh Khodabakhsh, Ali Asadnia, Alieyeh Sarabandi Moghaddam, Maryam Khademi, Majid Shakiba, Ali Maher, Elham Salehian

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引用次数: 1

Abstract

Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority.

Method and material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records.

Results: The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model.

Conclusion: Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.

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使用机器学习方法预测新冠肺炎糖尿病患者的住院死亡率。

背景：自2019年12月出现以来，直到2022年6月，2019冠状病毒（新冠肺炎）已经影响了全球各地的人口 ~ 5.35亿人离开 ~ 631万人死亡。这使得识别和预测新冠肺炎成为重要的医疗保健优先事项。方法和材料：本研究中使用的数据集来自德黑兰Shahid Beheshti医学科学大学，包括2019年10月8日至2021年3月8日期间住院的29817名新冠肺炎患者的信息。由于糖尿病已被证明是导致不良结果的重要因素，我们将重点放在了新冠肺炎糖尿病患者身上，为我们留下了2824份记录。结果：使用决策树算法对数据进行了分析，并挖掘了几个关联规则。所述决策树还用于预测患者的释放状态。我们使用准确性（87.07%）、敏感性（88%）和特异性（80%）作为我们模型的评估指标。结论：最初，这项研究提供了关于各种潜在疾病的新冠肺炎住院患者百分比的信息。据观察，糖尿病患者是风险最大的人群。因此，根据我们数据集得出的规则，我们发现年龄类别（51-80岁）、心肺复苏术和ICU住院在糖尿病住院患者的出院状态中起着关键作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Diabetes and Metabolic Disorders Medicine-Internal Medicine

CiteScore

4.80

自引率

3.60%

发文量

210

期刊介绍： Journal of Diabetes & Metabolic Disorders is a peer reviewed journal which publishes original clinical and translational articles and reviews in the field of endocrinology and provides a forum of debate of the highest quality on these issues. Topics of interest include, but are not limited to, diabetes, lipid disorders, metabolic disorders, osteoporosis, interdisciplinary practices in endocrinology, cardiovascular and metabolic risk, aging research, obesity, traditional medicine, pychosomatic research, behavioral medicine, ethics and evidence-based practices.As of Jan 2018 the journal is published by Springer as a hybrid journal with no article processing charges. All articles published before 2018 are available free of charge on springerlink.Unofficial 2017 2-year Impact Factor: 1.816.