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Diagnosing primary immunodeficiency diseases using health records and machine learning

Published: September 12, 2022

Primary immunodeficiency diseases (PIDD) cause patients to develop repeated, life-threatening infections because their immune systems do not work correctly. Regular treatment with antibodies, an important part of the immune system, can prevent infections and improve quality of life in many patients with PIDD. However, it takes an average of 6–9 years for patients to be diagnosed, leading to infections and deaths that could have been prevented with treatment.

In a study published in The Journal of Allergy and Clinical Immunology: In Practice, Dr Mayampurath and colleagues developed a model that used electronic health records and machine learning to help doctors diagnose PIDD early. Machine learning is the ability of computers to learn from data, allowing them to draw conclusions based on patterns in the data without any further instructions. To develop the model, the analyses first identified patients with PIDD who had received antibody treatments and, for comparison, patients with asthma being treated at the University of Chicago Medical Center. Electronic health records were then used to look for clues that patients might have had PIDD or asthma before they were diagnosed. These clues, such as medications, blood tests and chest X-rays, were used to train the machine learning model.

The electronic health records from 247 patients with PIDD and 6,175 patients with asthma were included in the study. After training, the final model was able to identify whether patients had PIDD or asthma using information from before they were diagnosed. In the future, this model could allow doctors to identify patients who are likely to have PIDD so that they can be diagnosed and treated at an early stage, which should lead to a better quality of life for these patients.

The Journal of Allergy and Clinical Immunology: In Practice is an official journal of the AAAAI, focusing on practical information for the practicing clinician.

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