The anterior cruciate ligament (ACL) is the most commonly injured ligament in the body, accounting for more than 200,000 ACL tears occurring annually in the US and upwards of 90% of patients choosing to undergo reconstruction surgery. After the reconstruction surgery, approximately 30% of youth patients go on to re-tear their ACL, and it has been proven that repeated ACL reconstructions not only have inferior results but can be devastating for all those involved. In this paper, we propose RT-ACL, a system that enhances patient outcomes by reducing their risk of an ACL re-tear by providing personalized recommendations of modifiable risk factors that can be altered during the patient's recovery process. Our system leverages the RT-ACL model that uses labeling functions designed by clinicians to classify the risk level of an ACL re-tear. Further, it identifies modifiable risk factors and suggests interventions to minimize adverse outcomes and complications. We evaluated our system on a dataset of 441 youth patients, 8–21 years of age, that underwent an ACL reconstruction at the Children's Hospital of Philadelphia. The results indicate patients classified as low risk re-tear at a rate of 12%, medium risk at a rate of 30%, and high risk re-tear at a rate of 59%. This demonstrates those classified by our system as high risk are 4.6 times as likely to re-tear their ACL than those classified as low risk.