A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer's disease and mild cognitive impairment
A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer's disease and mild cognitive impairment
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DOI：
10.1016/j.compbiomed.2014.04.010
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发表时间：
2014-08-01
影响因子：
7.7
通讯作者：
Muchaluat Saade, Debora Christina
中科院分区：
文献类型：
--
作者：
Seixas, Flavio Luiz;Zadrozny, Bianca;Muchaluat Saade, Debora Christina
人口老龄化已经成为一种全球现象，在发达国家和发展中国家都产生了不同的后果。神经退行性疾病，例如阿尔茨海默病（AD），在老年人群中发病率很高。此类疾病的早期诊断可以实现早期治疗并提高患者的生活质量。本文提出了一种贝叶斯网络决策模型，用于支持痴呆症、AD 和轻度认知障碍 (MCI) 的诊断。贝叶斯网络非常适合表示临床领域中存在的不确定性和因果关系。所提出的贝叶斯网络是结合专家知识和面向数据的建模进行建模的。该网络结构是根据当前的诊断标准和该领域专家医生的意见构建的。使用监督学习算法根据真实临床病例数据集估计网络参数。该数据集包含来自杜克大学医学中心（美国华盛顿）和阿尔茨海默病及相关疾病中心（巴西里约热内卢联邦大学精神病学研究所）的患者和正常对照的数据。数据集属性包括易感因素、神经心理学测试结果、患者人口统计数据、症状和体征。使用定量方法和敏感性分析评估决策模型。总之，与大多数其他众所周知的分类器相比，所提出的贝叶斯网络在痴呆症、AD 和 MCI 的诊断方面显示出更好的结果。此外，它还为医生提供了额外的有用信息，例如某些因素对诊断的贡献。 (C) 2014 Elsevier Ltd. 保留所有权利。
Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer's Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer's Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis. (C) 2014 Elsevier Ltd. All rights reserved.