Long COVID has emerged as a pandemic within the pandemic.
As scientists work to untangle the many remaining unanswered questions about how the initial infection impacts the body, they must now also investigate why some people develop debilitating, chronic symptoms that last months to years longer.
In a study from Lawrence Berkeley National Laboratory and elsewhere, scientists found a new machine learning tool may help tackle long COVID.
The software analyzed entries in electronic health records (EHRs) to find symptoms in common among people who have been diagnosed with long COVID and to define subtypes of the condition.
In the study, the team developed and validated their software using a database of EHR information from 6,469 patients diagnosed with long COVID after confirmed COVID-19 infections.
The researchers also found strong links between different long COVID subtypes and pre-existing conditions such as diabetes and high blood pressure.
According to the team, this research will help improve the understanding of how and why some individuals develop long COVID symptoms and may enable more effective treatments by helping clinicians develop tailored therapies for each group.
For example, the best treatment for patients experiencing nausea and abdominal pain might be quite different from treatment for those suffering from a persistent cough and other lung symptoms.
The researchers note that the tool will be convenient for researchers because the machine learning approach at its core self-adapts for different EHR systems, allowing researchers to gather data from a wide variety of medical establishments.
This research builds on previous work to develop the Human Phenotype Ontology, an open-access database and research tool that provides a standardized vocabulary of symptoms and features found in all human diseases.
If you care about COVID, please read studies about new drug to treat both COVID-19 and cancer, and Mediterranean diets could help people recover after COVID infection.
The study was conducted by Justin Reese et al and published in eBioMedicine.
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