According to the article, this is by analyzing patient records with machine learning.

The conditions that most influenced the prediction of Alzheimer’s were high cholesterol and, in the case of women, osteoporosis, a disease that weakens the bones.

The work demonstrates the promise of using artificial intelligence (AI) to detect patterns in clinical data that can then be used to scour large genetic databases to determine what is driving that risk.

“This is a first step toward using AI on routine clinical data, not only to identify risk as early as possible, but also to understand the biology behind it,” said study lead author Alice Tang, a student PhD at the Sirota Laboratory of the university center.

The power of this AI approach comes from identifying risks based on combinations of diseases, the experts explained.

Alzheimer’s, a progressive and fatal form of dementia that destroys memory, affects about 6.7 million Americans, nearly two-thirds of whom are women.

The risk of contracting the disease increases with age and women tend to live longer than men, but that does not fully explain why more women suffer from it than men.

The researchers used the clinical database of more than five million patients to search for co-occurring conditions in patients who had been diagnosed with Alzheimer’s.

They found that they could identify with 72 percent predictive power who would develop the disease up to seven years earlier.

Several factors, including hypertension, high cholesterol, and vitamin D deficiency, were predictive in both men and women.

Erectile dysfunction and prostate enlargement were also predictive for men.

But for women, osteoporosis was a particularly important predictor, which doesn’t mean that everyone with this bone disease, which is common among older women, will get Alzheimer’s.

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