脑卒中预后预测和治疗计划的统计模型
A Statistical Model for Stroke Outcome Prediction and Treatment Planning
摘要：
脑卒中是世界范围内死亡和长期残疾的主要原因。中风的预测结果模型对于个性化治疗、康复计划和对照临床试验是有价值的。在本文中，我们设计了一个新模型来预测短期结果，即几种治疗方法的假定治疗窗口。我们基于回归的模型具有参数形式，旨在解决医学数据集中常见的许多挑战，如高度相关的变量和类别不平衡。从经验上讲，我们的模型在预测短期结果和推断改善结果的最有效治疗方法方面优于最知名的先前模型。
英文摘要：
Stroke is a major cause of mortality and long--term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning and in controlled clinical trials. In this paper we design a new model to predict outcome in the short-term, the putative therapeutic window for several treatments. Our regression-based model has a parametric form that is designed to address many challenges common in medical datasets like highly correlated variables and class imbalance. Empirically our model outperforms the best--known previous models in predicting short--term outcomes and in inferring the most effective treatments that improve outcome.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
