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IBM is developing an artificial intelligence that predicts the progression of Parkinson's disease in each patient

IBM is developing an artificial intelligence that predicts the progression of Parkinson’s disease in each patient

Researchers from IBM and the Michael J. Fox Foundation have developed a model that can predict the course of Parkinson’s disease in humans. That is, when and in what form the symptoms will appear as the disease progresses.

Information that will be necessary to provide treatment to patients that helps control symptoms as the disease progresses.

They use artificial intelligence to improve the quality of life for Parkinson’s patients

This isn’t the first time we’ve seen how the potential of AI is being used to help Parkinson’s patients, but each initiative has a different perspective that is making great strides in improving patients’ quality of life.

For example, we’ve seen how a group of engineers from Imperial College are using artificial intelligence and a wearable device to monitor and treat symptoms of Parkinson’s disease. And now IA for IBM He suggests the same goal, but anticipate symptoms, and predict how the disease will progress.

Our goal is to use artificial intelligence to help manage patients and design clinical trials. These goals are important because despite the prevalence of Parkinson’s disease, patients experience a unique combination of motor and non-motor symptoms.

And for this, I collaborated with the Michael J. Fox Foundation, which made it possible to use an anonymous data set with information from more than 1,400 people.

The data set served as input to the machine learning approach, allowing complex symptoms and progression patterns to be discovered. […] While many previous studies have focused on the characterization of Parkinson’s disease using only basic information, our method is based on up to seven years of patient data.

Thanks to this, they have developed an artificial intelligence that collects the symptoms of the disease and establishes patterns to predict the development of the disease, given that it does not progress in the same way in all patients.

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