Barbini Emanuela, Cevenini Gabriele, Scolletta Sabino, Biagioli Bonizella, Giomarelli Pierpaolo, Barbini Paolo
Department of Surgery and Bioengineering, University of Siena, Siena, Italy.
BMC Med Inform Decis Mak. 2007 Nov 22;7:35. doi: 10.1186/1472-6947-7-35.
Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.
Models based on Bayes rule, k-nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.
Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. k-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.
Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.
最近提出了不同的方法来预测重症监护病房（ICU）的发病率。本研究的目的是严格审查一系列用于开发能够估计心脏手术后ICU发病概率模型的方法。该研究分为两个部分。在第一部分中，用于估计类别成员概率的常用模型根据其潜在的数学原理分为不同类别。从理论角度并考虑临床应用，分析和讨论了每个模型的建模技术以及内在的优缺点。
研究了基于贝叶斯规则、k近邻算法、逻辑回归、评分系统和人工神经网络的模型。描述了模型设计的关键问题。还包括了对模型结构某些方面的数学处理，供有兴趣开发模型的读者参考，不过如果读者仅从用户角度想了解模型假设、弱点和优势的实际意义，则无需完全理解数学关系。
评分系统因其使用简单而极具吸引力，尽管这可能会削弱其预测能力。逻辑回归模型是可靠的工具，尽管它们存在大多数回归程序的主要局限性。贝叶斯模型似乎是复杂性和预测性能之间的良好折衷，但通常需要对模型进行重新校准。k近邻可能是一种有效的非参数技术，不过计算成本和对大量数据存储的需求是该方法的主要弱点。人工神经网络相对于常见统计模型具有内在优势，尽管训练过程可能存在问题。
了解模型假设以及不同方法的理论优缺点对于设计估计心脏手术后发病概率的模型至关重要。然而，合理的选择还需要评估和比较本地开发的竞争模型在临床场景中的实际性能，以便在本地需求和模型响应之间获得令人满意的一致性。因此，在本研究的第二部分中，将在专门的ICU中获取的真实数据上测试上述预测模型。
