论文标题
神经符号神经退行性疾病模型作为概率编程的深内核
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
论文作者
论文摘要
我们提出了一种概率编程的深内核学习方法，以对神经退行性疾病的个性化，预测性建模。我们的分析考虑了一系列神经和符号机器学习方法，我们评估了预测性能和重要的医学AI特性，例如可解释性，不确定性推理，数据效率和利用域知识。我们的贝叶斯方法将高斯过程的灵活性与神经网络的结构力相结合，以建模生物标志物进行，而无需临床标签进行培训。我们对阿尔茨海默氏病预测的问题进行评估，从而产生的结果超过了预测神经退行性的准确性和及时性，以及贝叶斯非参数和概率编程的实际优势。
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches, which we assess for predictive performance and important medical AI properties such as interpretability, uncertainty reasoning, data-efficiency, and leveraging domain knowledge. Our Bayesian approach combines the flexibility of Gaussian processes with the structural power of neural networks to model biomarker progressions, without needing clinical labels for training. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning in both accuracy and timeliness of predicting neurodegeneration, and with the practical advantages of Bayesian nonparametrics and probabilistic programming.
