ArticleScientific reports2023
Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Generalized linear multiple kernel learning for high-dimensional image data.Statistics & probability letters · 2027Article
- CT-based radiomics models for predicting the prognosis of children with mycoplasma pneumonia.Frontiers in cellular and infection microbiology · 2026Article
- Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics.Radiological physics and technology · 2024Article
- Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.Harvard data science review · 2024Article
- Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.Frontiers in molecular biosciences · 2024Review
- Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine.Healthcare (Basel, Switzerland) · 2023Article
- MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis.Scientific reports · 2023Article
- The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest.Frontiers in immunology · 2023Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.