TY - JOUR
T1 - Prediction modeling—part 2 : using machine learning strategies to improve transplantation outcomes
AU - Coorey, Craig Peter
AU - Sharma, Ankit
AU - Muller, Samuel
AU - Yang, Jean Yee Hwa
PY - 2021
Y1 - 2021
N2 - Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key stages of a kidney transplant recipient's journey, from initial waitlisting and donor selection, to personalization of immunosuppression and prediction of post-transplantation events. Both unsupervised and supervised machine learning methods are presented, including k-means clustering, principal components analysis, k-nearest neighbors, and random forests. The various challenges of these approaches are also discussed.
AB - Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key stages of a kidney transplant recipient's journey, from initial waitlisting and donor selection, to personalization of immunosuppression and prediction of post-transplantation events. Both unsupervised and supervised machine learning methods are presented, including k-means clustering, principal components analysis, k-nearest neighbors, and random forests. The various challenges of these approaches are also discussed.
UR - https://hdl.handle.net/1959.7/uws:63034
U2 - 10.1016/j.kint.2020.08.026
DO - 10.1016/j.kint.2020.08.026
M3 - Article
SN - 1523-1755
SN - 0085-2538
VL - 99
SP - 817
EP - 823
JO - Kidney International
JF - Kidney International
IS - 4
ER -