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Title:

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning.

Document type:
Journal Article
Author(s):
Jakob, Carolin E M; Mahajan, Ujjwal Mukund; Oswald, Marcus; Stecher, Melanie; Schons, Maximilian; Mayerle, Julia; Rieg, Siegbert; Pletz, Mathias; Merle, Uta; Wille, Kai; Borgmann, Stefan; Spinner, Christoph D; Dolff, Sebastian; Scherer, Clemens; Pilgram, Lisa; Rüthrich, Maria; Hanses, Frank; Hower, Martin; Strauß, Richard; Massberg, Steffen; Er, Ahmet Görkem; Jung, Norma; Vehreschild, Jörg Janne; Stubbe, Hans; Tometten, Lukas; König, Rainer
Abstract:
PURPOSE: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. METHODS: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19....     »
Journal title abbreviation:
Infection
Year:
2022
Journal volume:
50
Journal issue:
2
Pages contribution:
359-370
Fulltext / DOI:
doi:10.1007/s15010-021-01656-z
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34279815
Print-ISSN:
0300-8126
TUM Institution:
1036; 1298; 606; Klinik und Poliklinik für Innere Medizin II, Gastroenterologie
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