Non‐homogeneous Markov Processes for Biomedical Data Analysis
Non‐homogeneous Markov Processes for Biomedical Data Analysis
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DOI：
10.1002/bimj.200310114
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发表时间：
2005-06
影响因子：
1.7
通讯作者：
Ricardo Ocañ-Riola
中科院分区：
文献类型：
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作者：
Ricardo Ocañ-Riola
不久前，马尔可夫过程被引入到生物医学科学中，以研究疾病历史事件。齐次和非齐次马尔可夫过程是随机过程研究的一个重要领域，特别是当精确的过渡时间未知和分析中存在区间截尾观察时。当齐次假设太强时，应使用非齐次马尔可夫过程。然而，这些类型的模型增加了分析的复杂性，并且标准软件是有限的。本文综述了非齐次马尔可夫模型的拟合方法，并提出了一种用于生物医学数据分析的算法。该方法已被用于分析乳腺癌数据。已经实现了用于此目的的特定软件。（©2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim）
Some time ago, the Markov processes were introduced in biomedical sciences in order to study disease history events. Homogeneous and Non‐homogeneous Markov processes are an important field of research into stochastic processes, especially when exact transition times are unknown and interval‐censored observations are present in the analysis. Non‐homogeneous Markov process should be used when the homogeneous assumption is too strong. However these sorts of models increase the complexity of the analysis and standard software is limited. In this paper, some methods for fitting non‐homogeneous Markov models are reviewed and an algorithm is proposed for biomedical data analysis. The method has been applied to analyse breast cancer data. Specific software for this purpose has been implemented. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)