"Mild to moderate asthma can be difficult to diagnose because symptoms change over time and can be complicated by other respiratory conditions," said Dr. Supinda Bunyavanich, physician and researcher at the Icahn School of Medicine. "Our nasal brush test takes seconds to collect -- for time-strapped clinicians, particularly primary care providers at the frontlines of asthma diagnosis, this could greatly improve patient outcomes through early and accurate diagnosis."
Currently, pulmonary function testing (PFT) is the most reliable diagnostic tool for asthma. However, access to the equipment and expertise needed to perform these tests is not always prevalent in primary care settings where asthma is frequently diagnosed and treated. It is also difficult to differentiate between asthma and other respiratory diseases using PFT alone, while the nasal brush and subsequent analysis for this asthma biomarker provides a binary result of asthma or not asthma.
Data - Scientists - Study - Machine - Algorithms
Data scientists leading the study applied machine learning algorithms to the genetic (RNA) data acquired from nasal brushes of patients with and without asthma. This robust data collection, and machine learning analysis identified a 90-gene biomarker indicative of asthma status. "One of the most exciting components of this study is demonstrating the power of machine learning when applied to biomedical data," said Dr. Gaurav Pandey, who led data science efforts to develop the biomarker. "Collaborations between computational scientists and biomedical researchers and clinicians are advancing medicine at an inspiring...
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