AI Model for Monitoring COVID-19 Predicts Mortality Within First 30 Days of Admission

In an article published in the Journal of the American Medical Informatics Association, the research team at the Universitat Politècnica de València (UPV, Valencia, Spain) has demonstrated the limitations that the variability or heterogeneity of data may have in reliably applying AI when it comes from multiple sources, e.g. a range of hospitals or countries. The article sets out the key aspects of potential solutions to such limitations. Furthermore, the team has developed new tools based on this study to help describe and classify patients with COVID-19.

The researchers have also developed an AI model for the early prediction of mortality (within the first 30 days of admission to the emergency department), focusing principally on adults aged over 50. They have also developed a deep learning application that helps to predict severity in all age groups, with the advantage of being able to operate even with incomplete patient information, offering robust and reliable AI in the event of data quality issues.

“These predictive models can help to select the best treatment for each patient according to their mortality risk, and to plan and manage resources in cases of low availability of resources, and in a way that can withstand potential uncertainties in the available information,” said Carlos Sáez, a member of the BDSLab-ITACA group research team at Universitat Politècnica de València, who coordinated the study.

In addition, following a study of nearly 800,000 COVID-19 cases, the researchers have developed a new technique to investigate subphenotypes (dividing patient populations into meaningful groups) in line with clinical characteristics. This technique, based on meta-clustering exploratory AI, can be used to automatically obtain a large number of results at different socio-demographic levels (by age group, gender, and combinations thereof), which would otherwise have to be carried out manually, involving additional work. This technique not only encourages non-discrimination, but also presents the results to the user in a detailed and intuitive manner, ready for exploration. Applying this technique to the cases led the team to conclude that chronological age alone cannot be used as a risk factor for severity, but rather must always be accompanied by comorbidities and even habits (physiological age).

“We also observed that, under equivalent clinical conditions, women have a higher recovery rate than men and, among older people, it is those aged over 100 who recover best. And we found that there is significant variability in recovery rates between different states in Mexico and also depending on the clinical institution,” concluded Carlos Sáez.

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