Graphs in biomedical image analysis and integrating medical imaging and non-imaging modalities : second International Workshop, GRAIL 2018 and first International Workshop, Beyond MIC 2018, held in conjunction with MICCAI 2018, Granada,...最新文献

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A Bayesian Disease Progression Model for Clinical Trajectories. 临床轨迹的贝叶斯疾病进展模型。

Graphs in biomedical image analysis and integrating medical imaging and non-imaging modalities : second International Workshop, GRAIL 2018 and first International Workshop, Beyond MIC 2018, held in conjunction with MICCAI 2018, Granada,...

Pub Date : 2018-09-01 Epub Date: 2018-09-16 DOI: 10.1007/978-3-030-00689-1_6

Yingying Zhu, Mert R Sabuncu

In this work, we consider the problem of predicting the course of a progressive disease, such as cancer or Alzheimer's. Progressive diseases often start with mild symptoms that might precede a diagnosis, and each patient follows their own trajectory. Patient trajectories exhibit wild variability, which can be associated with many factors such as geno-type, age, or sex. An additional layer of complexity is that, in real life, the amount and type of data available for each patient can differ significantly. For example, for one patient we might have no prior history, whereas for another patient we might have detailed clinical assessments obtained at multiple prior time-points. This paper presents a probabilistic model that can handle multiple modalities (including images and clinical assessments) and variable patient histories with irregular timings and missing entries, to predict clinical scores at future time-points. We use a sigmoidal function to model latent disease progression, which gives rise to clinical observations in our generative model. We implemented an approximate Bayesian inference strategy on the proposed model to estimate the parameters on data from a large population of subjects. Furthermore, the Bayesian framework enables the model to automatically fine-tune its predictions based on historical observations that might be available on the test subject. We applied our method to a longitudinal Alzheimer's disease dataset with more than 3,000 subjects [1] with comparisons against several benchmarks.

在这项工作中，我们考虑了预测进展性疾病(如癌症或阿尔茨海默氏症)病程的问题。进行性疾病通常在诊断之前就开始有轻微的症状，每个病人都有自己的发展轨迹。患者轨迹表现出巨大的变异性，这可能与许多因素有关，如基因型、年龄或性别。另一层复杂性是，在现实生活中，每个患者可用数据的数量和类型可能会有很大差异。例如，对于一个病人，我们可能没有先前的病史，而对于另一个病人，我们可能在多个先前的时间点获得详细的临床评估。本文提出了一个概率模型，该模型可以处理多种模式(包括图像和临床评估)和具有不规则时间和缺失条目的可变患者病史，以预测未来时间点的临床评分。我们使用s型函数来模拟潜伏性疾病的进展，这在我们的生成模型中引起了临床观察。我们在提出的模型上实现了近似贝叶斯推理策略，以估计来自大量受试者的数据的参数。此外，贝叶斯框架使模型能够根据可能在测试主题上可用的历史观察自动微调其预测。我们将我们的方法应用于3000多名受试者的纵向阿尔茨海默病数据集[1]，并与几个基准进行了比较。

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