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Many prognostic statistical tools have been developed for the prediction of disease severity, but it is still unclear which ones should be used in practice. We aim to guide clinicians in choosing the best available tools to make optimal decisions and assess their role in resource management and assess what can be learned from the COVID-19 scenario for development of prediction models in similar medical applications. Using the five major medical databases: MEDLINE (via PubMed), Embase, Cochrane Library (CENTRAL), Cochrane COVID-19 Study Register, and Scopus, we conducted a comprehensive systematic review of prediction tools between 2020 January and 2023 April for hospitalized COVID-19 patients. We identified both the relevant confounding factors of tool performance using the MetaForest algorithm and the best tools\u2014comparing linear, machine learning, and deep learning methods\u2014with mixed-effects meta-regression models. The risk of bias was evaluated using the PROBAST tool. Our systematic search identified eligible 27,312 studies, out of which 290 were eligible for data extraction, reporting on 430 independent evaluations of severity prediction tools with ~\u20092.8\u00a0million patients. Neural Network-based tools have the highest performance with a pooled AUC of 0.893 (0.748\u20131.000), 0.752 (0.614\u20130.853) sensitivity, 0.914 (0.849\u20130.952) specificity, using clinical, laboratory, and imaging data. The relevant confounders of performance are the geographic region of patients, the rate of severe cases, and the use of C-Reactive Protein as input data. 88% of studies have a high risk of bias, mostly because of deficiencies in the data analysis. All investigated tools in use aid decision-making for COVID-19 severity prediction, but Machine Learning tools, specifically Neural Networks clearly outperform other methods, especially in cases when the basic characteristics of severe and non-severe patient groups are similar, and without the need for more data. When highly specific biomarkers are not available\u2014such as in the case of COVID-19\u2014practitioners should abandon general clinical severity scores and turn to disease specific Machine Learning tools.<\/jats:p>","DOI":"10.1038\/s41598-025-95282-6","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:57:24Z","timestamp":1742961444000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep neural networks excel in COVID-19 disease severity prediction\u2014a meta-regression analysis"],"prefix":"10.1038","volume":"15","author":[{"given":"M\u00e1rton","family":"Rakovics","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanni Ad\u00e9l","family":"Meznerics","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u00e9ter","family":"Feh\u00e9rv\u00e1ri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tam\u00e1s","family":"K\u00f3i","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezs\u0151","family":"Csupor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e1s","family":"B\u00e1nv\u00f6lgyi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriella Anna","family":"Rapszky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie Anne","family":"Engh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u00e9ter","family":"Hegyi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Harnos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"key":"95282_CR1","unstructured":"Worldometers.info. 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