Machine Learning Tools for COVID-19 Patient Screening Discussed at AACC 2021 – HospiMedica

A team of researchers at the National Institute of Blood Disease (Karachi, Pakistan) have created a new machine learning tool that could help healthcare workers to quickly screen and direct the flow of COVID-19 patients arriving at hospitals. The results from an evaluation of this algorithm were presented at the 2021 AACC Annual Scientific Meeting & Clinical Lab Expo.
It is important for clinicians to quickly diagnose COVID-19 patients when they arrive at hospitals, both to triage them and to separate them from other vulnerable patients who may be immunocompromised or have pre-existing medical conditions. This can be difficult, however, because COVID-19 shares many symptoms with other viral infections, and the most accurate PCR-based tests for COVID-19 can take several days to yield results.
This led the researchers to create a machine learning algorithm to help healthcare workers efficiently screen incoming COVID-19 patients. The scientists extracted routine diagnostic and demographic data from the records of 21,672 patients presenting at hospitals and applied several statistical techniques to develop this algorithm, which is a predictive model that differentiates between COVID-19 and non-COVID-19 patients. During validation experiments, the model performed with an accuracy of up to 92.5% when tested with an independent dataset and showed a negative predictive value of up to 96.9%. The latter means that the model is particularly reliable when identifying patients who don’t have COVID-19.
“The true negative labeling efficiency of our research advocates its utility as a screening test for rapid expulsion of SARS-CoV-2 from emergency departments, aiding prompt care decisions, directing patient-case flow, and fulfilling the role of a ‘pre-test’ concerning orderly RT-PCR testing where it is not handy,” said Dr. Rana Zeeshan Haider, PhD who led the study. “We propose this test to accept the challenge of critical diagnostic needs in resource constrained settings where molecular testing is not under the flag of routine testing panels.”
Related Links:
National Institute of Blood Disease 

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