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Machine learning shows no difference in angina symptoms between men and women

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Machine learning shows no difference in angina symptoms between men and women

“This sophisticated machine learning study suggests, alongside several other recent more conventional studies, that there may be fewer if any differences in symptomatic presentation of heart attacks in women compared to men,” says Philippe Gabriel Steg, a professor of cardiology at Université Paris- Diderot and director of the Coronary Care Unit of Hôpital Bichat in Paris, France.
“This has important consequences in the organization of care for patients with suspected heart attacks, in whom diagnostic strategies probably need to be similar in women and men,” adds Steg, who was not involved with the MIT study.
Lensing offers a new look
The idea of applying machine learning to cardiology came when Catherine Kreatsoulas, then a Fulbright fellow and heart and stroke research fellow at the Harvard School of Public Health, met Dinakar after a talk in 2014 by noted linguist Noam Chomsky. An interest in language drew them both to the talk, and Kreatsoulas in particular was concerned about the differences in the way men and women express their symptoms, and how physicians might be understanding — or misunderstanding — the way men and women speak about their heart attack symptoms.
In the United States and Canada, 90 percent of cardiologists are male, and Kreatsoulas thought, “‘could this be a potential case of ‘lost in translation?’,” she says.
Kreatsoulas also was concerned that doctors might be misdiagnosing or underdiagnosing female patients — as well as men who didn’t express “typical” angina symptoms — “because doctors have this frame, given their years of medical training in cardiology, that men and women have different symptoms,” Dinakar explains.
Dinakar thought a machine learning framework called “lensing” that he had been working on for crisis counseling might offer a new way of understanding angina symptoms. In its simplest form, lensing acknowledges that different participants bring their own viewpoint or biases to a collective problem or conversation. By developing algorithms that include these different lenses, researchers can retrieve a more complete picture of the data provided by real-world conversations.
“When we train machine learning models in situations like the heart disease diagnosis, it is important for us to capture, in some way, the lens of the physician and the lens of the patient,” says Dinakar.
To accomplish this, the researchers audio-recorded two clinical interviews, one of patients describing their angina symptoms in clinical consult interviews with physicians and one of patient-research assistant conversations “to capture in their own natural words their descriptions of symptoms, to see if we could use methods in machine learning to see if there are a lot of differences between women and men,” he says.
In a typical clinical trial, researchers treat “symptoms as check boxes” in their statistical analyses, Dinakar notes. “The result is to isolate one symptom from another, and you don’t capture the entire patient symptomatology presentation — you begin to treat each symptom as if it’s the same across all patients,” says Dinakar.

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