Diagnosing Lung Disease with Help from Computers

APPLYING MACHINE LEARNING ALGORITHMS TO PATIENT DATA IS HELPING STANFORD RESEARCHERS BETTER DIAGNOSE AND TREAT LUNG DISEASE.

Parts of medicine can be trial and error-if one drug doesn’t work, try another; if a diagnosis isn’t leading to a cure, maybe the diagnosis is wrong. But eliminating that trial and error, through more informed diagnostic tests, saves time for both clinicians and patients. In the division of pulmonary, allergy and critical care medicine, machine learning algorithms are now guiding those more personalized treatment decisions.

In machine learning, a computer program sifts through data-whether it’s information on the levels of different molecules in a blood sample or scans of the lungs-and finds otherwise hidden patterns. Often, such programs can do a better job than the human eye at spotting structure in the data, finding correlations between data and patient outcomes, or pinpointing groups of variables that set some patients apart.

Many patients with pulmonary arterial hypertension (PAH) have other underlying diseases-scleroderma, lupus, cirrhosis, congenital heart disease, or HIV, to name a few. Others have been exposed to drugs or toxins, such as methamphetamine. And in roughly a third to half of patients, the rare lung disease appears without any explanation. In all cases, though, the underlying disease is the same: The small arteries that carry blood through the lungs narrow over time due to structural changes. This progression leads to high blood pressure in the lungs and places strain on the heart.

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