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Prediction of COVID-19 severity using machine learning.

作者信息

Karaduzovic-Hadziabdic Kanita, Adilovic Muhamed, Zhang Lu, Lumley Andrew I, Shah Pranay, Shoaib Muhammad, Satagopam Venkata, Srivastava Prashant Kumar, Emanueli Costanza, Greco Simona, Madè Alisia, Padro Teresa, Domingo Pedro, Lustrek Mitja, Scholz Markus, Rosolowski Maciej, Jordan Marko, Benczik Bettina, Ágg Bence, Ferdinandy Péter, Baker Andrew H, Fagherazzi Guy, Ollert Markus, Michel Joanna, Sanchez Gabriel, Firat Hüseyin, Brandenburger Timo, Martelli Fabio, Badimon Lina, Devaux Yvan

机构信息

Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina.

Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg.

出版信息

Clin Transl Med. 2024 Oct;14(10):e70042. doi: 10.1002/ctm2.70042.

DOI:
10.1002/ctm2.70042
PMID:39370709
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11456675/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/beadd55653bd/CTM2-14-e70042-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/74fbaf2b9667/CTM2-14-e70042-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/2b6ee54d11e7/CTM2-14-e70042-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/c34d8df6a15b/CTM2-14-e70042-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/beadd55653bd/CTM2-14-e70042-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/74fbaf2b9667/CTM2-14-e70042-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/2b6ee54d11e7/CTM2-14-e70042-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/c34d8df6a15b/CTM2-14-e70042-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/857e/11456675/beadd55653bd/CTM2-14-e70042-g003.jpg

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本文引用的文献

1
Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.基于长链非编码 RNA 的机器学习模型预测 COVID-19 住院患者死亡率的建立。
Nat Commun. 2024 May 20;15(1):4259. doi: 10.1038/s41467-024-47557-1.
2
Non-coding RNAs as therapeutic targets and biomarkers in ischaemic heart disease.非编码RNA作为缺血性心脏病的治疗靶点和生物标志物
Nat Rev Cardiol. 2024 Aug;21(8):556-573. doi: 10.1038/s41569-024-01001-5. Epub 2024 Mar 18.
3
Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection.
开发 SARS-CoV-2 感染后后遗症的定义。
JAMA. 2023 Jun 13;329(22):1934-1946. doi: 10.1001/jama.2023.8823.
4
FIMICS: A panel of long noncoding RNAs for cardiovascular conditions.FIMICS：一组用于心血管疾病的长链非编码RNA
Heliyon. 2023 Jan 20;9(1):e13087. doi: 10.1016/j.heliyon.2023.e13087. eCollection 2023 Jan.
5
Leveraging non-coding RNAs to fight cardiovascular disease: the EU-CardioRNA network.利用非编码RNA对抗心血管疾病：欧盟心血管RNA网络
Eur Heart J. 2021 Dec 21;42(48):4881-4883. doi: 10.1093/eurheartj/ehab326.
6
Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129.心血管 RNA 标志物和人工智能可能改善 COVID-19 结局：来自 EU-CardioRNA COST 行动 CA17129 的立场文件。
Cardiovasc Res. 2021 Jul 7;117(8):1823-1840. doi: 10.1093/cvr/cvab094.
7
Transcriptomics Research to Improve Cardiovascular Healthcare.改善心血管健康的转录组学研究
Eur Heart J. 2020 Sep 14;41(35):3296-3298. doi: 10.1093/eurheartj/ehaa237.
8
Catalyzing Transcriptomics Research in Cardiovascular Disease: The CardioRNA COST Action CA17129.推动心血管疾病的转录组学研究：CardioRNA欧洲科学与技术合作组织行动CA17129
Noncoding RNA. 2019 Mar 29;5(2):31. doi: 10.3390/ncrna5020031.