Conference Coverage

Artificial intelligence hastens review for asthma risk

 

Key clinical point: Using natural language processing, a form of artificial intelligence that trains computers to discern natural human language, accurately and quickly identified asthma risk factors.

Major finding: In 50 minutes, a computer program performed the risk review that took 7 hours for manual chart review, with positive and negative predictive values for most risk factors in the 90% to 100% range.

Study details: A sample of charts for patients in the Olmsted County Birth Cohort, some analyzed with natural language processing and some reviewed manually.

Disclosures: No disclosures.

Source: Wi C AAAAI/WAO Joint Congress 2018 abstract 637.

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Susan Millard, MD, FCCP

Susan Millard, MD, FCCP, comments: This article brings mixed emotions. On one hand, using artificial intelligence brings a more thorough evaluation regarding asthma risk. On the other hand, our pediatric pulmonary subspecialty has gotten diluted over the last 3 decades. We used to regularly do arterial puncture, thoracentesis, and chest tube placement procedures. Now a computer might replace another aspect of our job, too? The practice of medicine is an art and that art should not be lost.


 

REPORTING FROM AAAAI/WAO JOINT CONGRESS 2018

– Reviewing patient charts for asthma risk factors using natural language processing can be done 8 times faster than reviewing the charts by hand, and with high levels of accuracy, researchers reported here.

Dr. Chung-Il Wi Thomas R. Collins/Frontline Medical News

Dr. Chung-Il Wi

Natural language processing (NLP) is a kind of artificial intelligence in which computers are “trained” through a reiterative process to understand human language.

Researchers at Mayo Clinic previously have shown that a program created in-house can successfully and quickly determine patients’ asthma status. In this study, they turned to assessment of asthma risk factors, Chung-Il Wi, MD, assistant professor of pediatrics at Mayo said in a presentation at the joint congress of the American Academy of Allergy, Asthma and Immunology and the World Asthma Organization.

They used a convenience sample of 177 patient charts to train the NLP system. The system extracted – from key terms and sentences in the electronic health record (EHR) – data such as breastfeeding history and history of atopic conditions such as allergic rhinitis, eczema, and food allergy. From parent charts, the system extracted terms related to family history of asthma and other atopic conditions. The performance of the NLP algorithm was assessed by comparison with results of a manual chart review in a test cohort of 220 patient charts.

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