ArticleInternational journal of imaging systems and technology2022
Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning.
Article in International journal of imaging systems and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
3 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
- Optimized robust learning framework based on big data for forecasting cardiovascular crises.Scientific reports · 2024Article
- Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning.International journal of imaging systems and technology · 2022Article
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Authors and funding
3 authors.
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Abstract
The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD
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