A spatio-temporal Bayesian network classifier for understanding visual field deterioration
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
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DOI：
10.1016/j.artmed.2004.07.004
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发表时间：
2005-06-01
影响因子：
7.5
通讯作者：
Garway-Heath, D
中科院分区：
文献类型：
--
作者：
Tucker, A;Vinciotti, V;Garway-Heath, D
目的：视野的渐进性改变是许多眼科疾病的特征，例如青光眼，青光眼是世界上不可逆失明的主要原因。最近，存储在患有视力退化的患者身上的数据量激增，包括现场测试数据、视网膜图像数据和患者人口统计数据。然而，在为这些数据所共有的空间和时间关系建立模型方面的工作相对较少。在本文中，我们介绍了一种新的方法来分类的视野（VF）数据，明确这些空间和时间的关系模型。方法：我们进行了我们提出的时空分析。贝叶斯分类器，并将其与机器学习和统计社区的一些分类器进行比较。所有这些都在VF和临床数据的两个数据集上进行了测试。我们调查的接收器工作特性曲线，由此产生的网络结构，并利用现有的解剖知识的眼睛，以验证所发现的模型。结果如下：结果是非常令人鼓舞的，表明我们的分类器与现有的统计模型相媲美，同时也有利于理解VF数据中的潜在空间和时间关系。研究结果揭示了在眼科数据库中使用这种模型进行知识发现的潜力，例如反映“鼻步”的网络，这是青光眼发病的早期指标。结论：本文中概述的结果为涉及许多其他空间和时间数据集（包括视网膜图像和临床数据）的实质性研究计划铺平了道路。© 2004 Elsevier B.V.保留所有权利。
Objective: Progressive toss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying visual field (VF) data that explicitly models these spatial and temporal relationships. Methodology: We carry out an analysis of our proposed spatio-temporal. Bayesian classifier and compare it to a number of classifiers from the machine learning and statistical communities. These are all tested on two datasets of VF and clinical data. We investigate the receiver operating characteristics curves, the resulting network structures and also make use of existing anatomical knowledge of the eye in order to validate the discovered models. Results: Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the 'nasal step', an early indicator of the onset of glaucoma. Conclusion: The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data. © 2004 Elsevier B.V. All rights reserved.