Machine learning, natural language processing used to generate clinical labels of medical scans

Researchers have conducted a study at the Icahn School of Medicine at Mount Sinai and published findings in journal Radiology wherein they have revealed how machine learning techniques, including natural language processing algorithms have been used to identify clinical concepts in radiologist reports for CT scans.

Scientists are optimistic about their technology and study findings and claim it to be the important first step in the development of artificial intelligence that could interpret scans and diagnose conditions.

Artificial intelligence could one day help radiologists interpret X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) studies. But for the technology to be effective in the medical arena, computer software must be “taught” the difference between a normal study and abnormal findings.

This study aimed to train this technology how to understand text reports written by radiologists. Researchers created a series of algorithms to teach the computer clusters of phrases. Examples of terminology included words like phospholipid, heartburn, and colonoscopy.

Researchers trained the computer software using 96,303 radiologist reports associated with head CT scans performed at The Mount Sinai Hospital and Mount Sinai Queens between 2010 and 2016. To characterize the “lexical complexity” of radiologist reports, researchers calculated metrics that reflected the variety of language used in these reports and compared these to other large collections of text: thousands of books, Reuters news stories, inpatient physician notes, and Amazon product reviews.

Deep learning describes a subcategory of machine learning that uses multiple layers of neural networks (computer systems that learn progressively) to perform inference, requiring large amounts of training data to achieve high accuracy. Techniques used in this study led to an accuracy of 91 percent, demonstrating that it is possible to automatically identify concepts in text from the complex domain of radiology.

Shirley King

About Shirley King

Shirley initially started out as a freelancer, before joining Healthcare Journal as a full-time contributor. Shirley is a versatile news contributor and can cover news stories in any subject thrown at her. You can reach her at our contact us page here.

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