Syed Zeeshan, Rubinfeld Ilan
University of Michigan, 2260 Hayward St, Ann Arbor, MI 48109, USA.
Henry Ford Health System, Detroit, MI, USA.
Per Med. 2010 Nov;7(6):695-701. doi: 10.2217/pme.10.69.
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.
即使接受类似的手术，接受手术的患者仍表现出高度可变的死亡和发病风险。准确量化这种风险对于术前决策至关重要，以确保患者接受最适合其个人情况的治疗，并指导术中及术后护理。尽管这个问题受到了相当多的关注，但现有的手术风险分层模型仍然基于传统统计方法以及多年来没有显著发展的问题陈述。本文探讨了机器学习和数据挖掘方面的最新创新，以推进这些工作。基于复杂计算技术的风险分层模型有望成为新一代高度准确且可广泛应用的预测分析工具。
