Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction.

Kyle N. Kunze,Evan M. Polce,Anil S. Ranawat,Per-Henrik Randsborg,Riley J. Williams,Answorth A. Allen,Benedict U. Nwachukwu,Andrew D. Pearle,Beth E. Shubin Stein,David M. Dines,Anne M. Kelly,Bryan T. Kelly,Howard A. Rose,Michael J. Maynard,Sabrina M. Strickland,Struan H. Coleman,Jo A. Hannafin,John D. MacGillivray,Robert G. Marx,Russell F. Warren,Scott A. Rodeo,Stephen Fealy,Stephen J. OBrien,Thomas L. Wickiewicz,Joshua S. Dines,Frank A. Cordasco,David Altcheck

Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction.

2021

Background:Understanding specific risk profiles for each patient and their propensity to experience clinically meaningful improvement after anterior cruciate ligament reconstruction (ACLR) is impor...

Keywords:

Medicine
Minimal clinically important difference
Anterior cruciate ligament
Physical medicine and rehabilitation
Anterior cruciate ligament reconstruction

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