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A group of Northeastern researchers is tapping into the power of machine learning to develop new models for identifying patients who may have post-acute sequelae of SARS-CoV-2 infection, or so-called "long COVID." Using electronic health records from the National COVID Cohort Collaborative, a federal database that compiles medical information about COVID-19 patients, researchers were able to develop models that helped identify COVID long haulers across a range of features--from past COVID diagnosis, to the types of medications they've been prescribed, according to new research published in Lancet Digital Health. The data harmonization effort drew from a variety of information sources to construct a picture of what long COVID looks like in the U.S.--and who is most likely to have it. Those sources include demographic data, healthcare visit details, diagnoses and medications for 97,995 adults with COVID-19, the study says. Patients most likely suffering from the post-infection illness, which is estimated to plague between 10-30% of people who contract COVID-19, are often characterized as having new or lingering symptoms that are present 90 days after being diagnosed with the viral infection--a criteria researchers also used to determine their base population in their analysis.