Machine Learning Helps Match Liver Transplant Donors With Organ Recipients


Machine learning is becoming more and more sophisticated as technology also advances. One of its latest achievements is helping liver patients find a match from liver donors by streamlining the process.

Two medical specialists at the Austin Health's Liver Transplant Unit in Melbourne, Australia developed an algorithm that matches potential liver donor with patients. The specialists said that they modeled the algorithm from the dating site, eHarmony, hoping to make the process faster which will, eventually, save more lives.

Bob Jones, director of the liver transplant unit, and Lawrence Lau said that they used 25 different features of donors and recipients and plug them into the algorithm to create faster and better outcome. Those features include the basic information, such as sex, age, and blood type, as well as certain characteristics the donors have.

After that, they used the machine learning algorithm to assess the results of 75 patients who have undergone transplants. The algorithm is 84 percent accurate compared to the traditional method of matching donors and recipients which is only 68 percent.

Jones said that this was the first time for them to asses the suitability of the liver compared to the current method where the specialist has to do all the work to assess everything.

Although the first stage was successful, Jeremy Chapman, the director of renal transplant at the Westmead Hospital, said that although the result is positive, the information should be used to inform the decision but not to make the decision.

Jones agreed saying their research is still in its early stages but he added that they want to add a thorough process that will include quantifiable considerations. They said that they want to use it in a randomized clinical trial with ethical approval.

The study, titled "Machine Learning Algorithms Predict Graft Failure," has been submitted to a number of medical journals.

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