论文标题
隐藏的马尔可夫模型作为复发性神经网络：阿尔茨海默氏病的应用
Hidden Markov models as recurrent neural networks: an application to Alzheimer's disease
论文作者
论文摘要
当真正的患者健康状态尚不完全了解时，隐藏的马尔可夫模型（HMM）通常用于疾病进展模型。由于HMM通常具有多个局部Optima，因此结合其他患者协变量可以改善参数估计和预测性能。为此，我们开发了隐藏的马尔可夫经常性神经网络（HMRNNS），这是一种复发性神经网络的特殊情况，将神经网络的灵活性与HMMS的可解释性相结合。可以将HMRNN简化为标准HMM，具有相同的可能性函数和参数解释，但也可以将HMM与其他预测性神经网络相结合，以将患者信息作为输入。 HMRNN通过梯度下降同时估算所有参数。使用阿尔茨海默氏病患者的数据集，我们演示了HMRNN如何将HMM与其他预测性神经网络结合起来，以改善疾病的预测并提供与通过预期最大化训练的标准HMM相比，提供了新颖的临床解释。
Hidden Markov models (HMMs) are commonly used for disease progression modeling when the true patient health state is not fully known. Since HMMs typically have multiple local optima, incorporating additional patient covariates can improve parameter estimation and predictive performance. To allow for this, we develop hidden Markov recurrent neural networks (HMRNNs), a special case of recurrent neural networks that combine neural networks' flexibility with HMMs' interpretability. The HMRNN can be reduced to a standard HMM, with an identical likelihood function and parameter interpretations, but it can also combine an HMM with other predictive neural networks that take patient information as input. The HMRNN estimates all parameters simultaneously via gradient descent. Using a dataset of Alzheimer's disease patients, we demonstrate how the HMRNN can combine an HMM with other predictive neural networks to improve disease forecasting and to offer a novel clinical interpretation compared with a standard HMM trained via expectation-maximization.
