Now ML Algorithms Can Help Diagnose Diseases

By Aaryaa Padhyegurjar

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According to a recent study, machine learning algorithms can assist medical professionals in differentiating between acute cholangitis and alcohol-associated hepatitis.

Researchers demonstrate how algorithms may be useful predicting tools utilising a few straightforward factors and frequently accessible structured clinical information in a paper that appears in Mayo Clinic Proceedings. “This study was motivated by seeing many medical providers in the emergency department or ICU struggle to distinguish acute cholangitis and alcohol-associated hepatitis, which are very different conditions that can present similarly,” says Joseph Ahn, M.D., a third-year gastroenterology and hepatology fellow at Mayo Clinic in Rochester. Dr. Ahn is first author of the study.

“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,” Dr. Ahn says. “The machine-learning algorithms demonstrated excellent performances for discriminating the two conditions, with over 93% accuracy.”

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459 people over the age of 18 had their electronic health records examined by researchers. Acute cholangitis or alcohol-associated hepatitis were the patients’ diagnoses. The eight machine-learning algorithms that were trained on these data. It was discovered that the algorithms performed better than doctors who took part in an online survey, as detailed in the article.

“The study highlights the potential for machine-learning algorithms to assist in clinical decision-making in cases of uncertainty,” says Dr. Ahn. “There are many instances of gastroenterologists receiving consults for urgent endoscopic retrograde cholangiopancreatography in patients who initially deny a history of alcohol use but later turn out to have alcohol-associated hepatitis. In some situations, the inability to obtain a reliable history from patients with altered mental status or lack of access to imaging modalities in underserved areas may force providers to make the determination based on a limited amount of objective data.”

According to the study, the machine-learning algorithms may assist medical workers who are urgently presented with an acutely ill patient who has abnormal liver enzymes if they can be made simple to use with an online calculator or smartphone app.


 

 

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