ArticleDiagnostics (Basel, Switzerland)2021
A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.Frontiers in public health · 2024Pooled it
- Article
- A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness.Annals of translational medicine · 2022Article
- Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes.Computer methods and programs in biomedicine · 2022Article
- An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review.Diagnostics (Basel, Switzerland) · 2022Review
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
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Authors and funding
13 authors.
Funding
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Abstract
backgroundAlthough several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers.
methodsWe propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index.
resultsOur results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9.
conclusionsto our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints.
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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.