Researchers at the University of Montreal (UdeM) and its Montreal Polytechnic engineering school in Canada are developing a computerized machine-learning method for better predicting how well a typical organ transplant will proceed.
The risk calculator the researchers aim to design via machine learning would be used by physicians and patients to decide whether an organ is well suited to the recipient.
They are employing the U.S. Scientific Registry of Transplant Recipients database to retrospectively review all U.S. patients who received a new kidney over 15 years, comparing old and new survival time modeling methods.
The next step is classifying the information physicians and patients use to a make better organ-transplant choices based on risk.
UdeM's Heloise Cardinal thinks machine learning will be an improvement over statistical analysis in weighing the myriad interactions between donated organs and recipients.
UdeM's Andrea Lodi expects their research to be a game-changer in improving the accuracy of organ-transplant predictions.
From UdeM News
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