Machine learning insights into patient satisfaction following lateral lumbar interbody fusion

Abstract

Functional outcomes, particularly improvements in low back pain, walking ability, and mental health, are the primary determinants of patient satisfaction following LLIF surgery. In contrast, surgical factors play a less significant role. Mental health emerged as a critical factor, underscoring the importance of addressing psychological recovery through preoperative counseling and personalized postoperative care. The analysis demonstrated that ML models, especially Random Forest, are effective tools for identifying the factors most predictive of postoperative satisfaction. These findings highlight the potential of ML techniques to enhance personalized treatment planning and improve outcomes by focusing on both physical and mental recovery. Further research, including multi-center studies and the integration of psychological variables, is needed to provide a more comprehensive understanding of patient satisfaction after LLIF surgery.

Ähnliche Arbeiten