Machine learning algorithms utilizing blood parameters enable early detection of immunethrombotic dysregulation in COVID‐19

From a more pragmatic perspective, the early detection of patients who may experience rapid clinical deterioration will enable prompt interventions and avert disease progression.1 T cell exhaustion, immunothrombotic dysregulation, as well as complement-associated microvascular injury are considered...

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Veröffentlicht in:	Clinical and translational medicine 2021-09, Vol.11 (9), p.e523-n/a, Article 523
Hauptverfasser:	Zhou, Zhaoming, Zhou, Xiang, Cheng, Liming, Wen, Lei, An, Taixue, Gao, Heng, Deng, Hongrong, Yan, Qi, Zhang, Xinlu, Li, Youjiang, Liao, Yixing, Chen, Xin‐zu, Nie, Bin, Cheng, Jie, Deng, Guanhua, Wang, Shengqiang, Li, Juan, Yin, Hanqi, Zhang, Mengxian, Cai, Linbo, Zheng, Lei, Li, Minglun, Jones, Bleddyn, Chen, Longhua, Abdollahi, Amir, Zhou, Meijuan, Zhou, Ping‐Kun, Zhou, Cheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Blood platelets Coronaviruses COVID-19 Cytokines Decision making Discriminant analysis Ethics Gene expression Generalized linear models Hemoglobin Letter to Editor Life Sciences & Biomedicine Lymphocytes Machine learning Medical laboratories Medical prognosis Medicine, Research & Experimental Neutrophils Oncology Research & Experimental Medicine Science & Technology Severe acute respiratory syndrome coronavirus 2
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