Bidirectional Inference Networks：一类用于健康分析的深度贝叶斯网络
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
摘要：
我们考虑推断任意一组变量的值的问题（例如，疾病的风险）给定其它观察到的变量（例如，症状和诊断的疾病）和高维信号（例如，MRI图像或EEG）。这是医疗保健中的常见问题，因为不同患者的感兴趣变量通常不同。现有的方法，包括贝叶斯网络和结构化预测，要么不包括高维信号或无法建模变量之间的条件依赖关系。为了解决这些问题，我们提出了双向推理网络（BIN），它将多个概率神经网络缝合在一起，每个网络都对条件依赖性进行建模。然后通过使用反向传播（BP）迭代更新变量以最大化相应的后验概率来进行预测。此外，我们将BIN扩展为复合BIN（CBIN），它涉及训练阶段的迭代预测过程，并通过自适应平滑优化景观来提高精度和计算效率。在合成和真实世界数据集（睡眠研究和皮肤病学数据集）上的实验表明，CBIN是一个单一的模型，可以实现最先进的性能，并在大多数推理任务中获得比多个模型更好的准确性，每个模型都专门针对不同的任务进行训练。
英文摘要：
We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.
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