Introduction. Instability remains a common complication following total hip arthroplasty (THA) and continues to account for the highest percentage of revisions in numerous registries. Many risk factors have been described, yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to apply a machine learning algorithm to develop a patient-specific risk score capable of dynamic adjustment based on operative decisions. Methods. 22,086 THA performed between 1998–2018 were evaluated. 632 THA sustained a postoperative dislocation (2.9%). Patients were robustly characterized based on non-modifiable factors: demographics, THA indication, spinal disease, spine surgery, neurologic