Personalized risk stratification for adverse surgical outcomes: innovation at the boundaries of medicine and computation. (November 2010) - Syed, Zeeshan; Rubinfeld, Ilan
Patients undergoing surgery exhibit a highly variable risk of mortality and morbidity, even when undergoing similar procedures. Accurately quantifying this risk is critical for preoperative decision-making to ensure patients recieve treatment that is optimal for their individual profile, and for guiding intraoperative and postoperative care. Despite the considerable attention this issue has received, existing models for surgical risk stratification remain grounded in traditional statistical methods and in problem statements that have not evolved significantly over the years. This article explores recent innovations in machine learning and data mining to advance these efforts. Risk-stratification models based on sophisticated computational techniques hold the promise of a new generation of predictive analytical tools that are highly accurate and widely deployable.