AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania's First Wave of COVID-19.

IF 3.8 3区医学 Q2 ENGINEERING, BIOMEDICAL

Bioengineering Pub Date : 2024-12-15 DOI:10.3390/bioengineering11121272

Sonja C S Simon, Igor Bibi, Daniel Schaffert, Johannes Benecke, Niklas Martin, Jan Leipe, Cristian Vladescu, Victor Olsavszky

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引用次数: 0

Abstract

Background: The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises.

Methods: AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January-September 2020).

Results: For discharge outcomes, Light Gradient Boosted models achieved an F1 score of 0.9644, while for mortality 0.7545 was reached. A Generalized Linear Model blender achieved an F1 score of 0.9884 for "acute or emergency" cases, and an average blender reached 0.923 for COVID-19 cases. Older age, specific hospitals, and oncology wards were less associated with improved recovery rates, while mortality was linked to abnormal lab results and cardiovascular/respiratory diseases. Patients admitted without referral, or patients in hospitals in the central region and the capital region of Romania were more likely to be acute cases. Finally, counties such as Argeş (South-Muntenia) and Brașov (Center) showed higher COVID-19 infection rates regardless of age.

Conclusions: AutoML provided valuable insights into patient outcomes, highlighting variations in care and the need for targeted health strategies for both COVID-19 and other health challenges.

查看原文本刊更多论文

在罗马尼亚第一波COVID-19期间对患者结果和紧急护理的自动驱动洞察。

背景：COVID-19大流行严重影响了医疗保健系统，影响了患者预后和资源分配。本研究应用自动机器学习（AutoML）来分析出院条件、死亡率和COVID-19病例等关键卫生产出，目的是改善对未来危机的应对。方法：使用AutoML在涵盖罗马尼亚第一波COVID-19（2020年1月至9月）的ICD-10数据集上训练和验证模型。结果：光梯度增强模型的出院结局F1得分为0.9644，死亡率F1得分为0.7545。广义线性模型混合器对“急性或紧急”病例的F1得分为0.9884，对COVID-19病例的平均混合器得分为0.923。年龄较大、特定医院和肿瘤病房与提高康复率的相关性较小，而死亡率与异常实验室结果和心血管/呼吸系统疾病有关。未经转诊而入院的病人或罗马尼亚中部地区和首都地区医院的病人更有可能是急性病例。最后，阿尔盖伊夫（南蒙尼尼亚）和Brașov（中部）等县不分年龄都出现了较高的新冠病毒感染率。结论：AutoML提供了有关患者预后的宝贵见解，突出了护理的差异以及针对COVID-19和其他卫生挑战制定有针对性的卫生战略的必要性。

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

Bioengineering Chemical Engineering-Bioengineering

CiteScore

4.00

自引率

8.70%

发文量

661

期刊介绍： Aims Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal: ● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings. ● Manuscripts regarding research proposals and research ideas will be particularly welcomed. ● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. ● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds. Scope ● Bionics and biological cybernetics: implantology; bio–abio interfaces ● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices ● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc. ● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology ● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering ● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation ● Translational bioengineering