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ML Algorithms Help Healthcare Staff Diagnose Alcohol-Associated Hepatitis, Acute Cholangitis


Said Mayo Clinic's Dr. Joseph Ahn, "We developed and trained machine learning algorithms to distinguish the two conditions using some of the routinely available lab values that all of these patients should have. The machine learning algorithms demonstrate

Credit: Mayo Clinic

A study by researchers at the Mayo Clinic and South Korea's Hanyang University found that machine learning algorithms can help healthcare providers distinguish between acute cholangitis and alcohol-associated hepatitis, which have similar symptoms.

The researchers developed and trained eight machine learning algorithms using data from 260 patients with alcohol-associated hepatitis and 194 with acute cholangitis.

Said Mayo Clinic's Dr. Joseph Ahn, "The machine-learning algorithms demonstrated excellent performances for discriminating the two conditions, with over 93% accuracy."

The researchers also found that the algorithms outperformed physicians who took part in an online survey.

Ahn said making these algorithms easily accessible via an online calculator or smartphone app "would lead to improved diagnostic accuracy and reduce the number of additional tests or inappropriate ordering of invasive procedures, which may delay the correct diagnosis or subject patients to the risk of unnecessary complications."

From Mayo Clinic
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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


 

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