Interpretable personalized surgical recommendation with joint consideration of multiple decisional dimensions

Abstract

Surgical planning can be highly complicated and personalized, where a surgeon needs to balance multiple decisional dimensions including surgical effectiveness, risk, cost, and patient’s conditions and preferences. Turning to artificial intelligence is a great appeal. This study filled in this gap with Multi-Dimensional Recommendation (MUDI), an interpretable data-driven intelligent system that supported personalized surgical recommendations on both the patient’s and the surgeon’s side with joint consideration of multiple decisional dimensions. Applied to Pelvic Organ Prolapse, a common female disease with significant impacts on life quality, MUDI stood out from a crowd of competing methods and achieved excellent performance that was comparable to top urogynecologists, with a transparent process that made communications between surgeons and patients easier. Users showed a willingness to accept the recommendations and achieved higher accuracy with the aid of MUDI. Such a success indicated that MUDI had the potential to solve similar challenges in other situations.