| Title: | Machine learning from complex and genetic data |
| Lead Institution: | University of California, San Francisco |
| Principal investigator: | Dr James Pirruccello |
Application 41664
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About
People take medical tests because doctors can diagnose disease based on the patterns seen in those tests. Some tests give simple results that can be easily interpreted by an expert. For example, a cardiologist can use lab tests that look at the levels of heart enzymes to better understand if a patient with chest pain is having a heart attack. This type of lab test gives back just one number. Other tests give back data that are more complicated. The ECG, which takes electrical measurements of the heart, was invented in 1895 and standardized around 1942. This type of test returns a number representing the strength of the electrical signal at various parts of the body. The device does this hundreds of times per second. When these numbers are graphed, a picture of the electrical activity at the surface of the heart can be understood. These can be interpreted by a cardiologist to diagnose many different diseases. However, there may still be diseases that could be diagnosed by ECG that we do not yet know about. For example, in 1992, 50 years after the ECG was standardized in its modern form, two brothers recognized a new pattern in ECG that went along with sudden cardiac death. The pattern that they found is a very memorable one, but there may be numerous less memorable patterns that we might be able to use to predict disease. Newer tests give back even more complicated data. Cardiac MRI images contain thousands of times more data than an ECG. Important patterns have been recognized in these images, such as certain patterns that are seen after heart attack. We think that there are probably many more important patterns left to discover. This is a challenging task for humans, but we think that machines that are exposed to tens of thousands of these images may help find some of these patterns. We would like to understand whether some of these patterns, in combination with genetics and traditional risk factors, can help us identify people who would benefit from doing something: for example, getting pictures taken more frequently, or taking a drug before they develop a disease.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 13001 | The AORTA Gene score for detection and risk stratification of ascending aortic dilation | James P Pirruccello (+11) | 2024 | European Heart Journal |
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