标题： RealDiffFusionNet：使用真实世界数据进行疾病进展建模的神经受控微分方程多头注意力融合网络 显示英文标题

标题： RealDiffFusionNet: Neural Controlled Differential Equation Informed Multi-Head Attention Fusion Networks for Disease Progression Modeling Using Real-World Data

摘要： 本文提出了一种名为RealDiffFusionNet的新型基于深度学习的方法，结合了神经控制微分方程（Neural CDE）- 时间序列模型，这些模型在处理不规则采样数据时具有鲁棒性，并结合多头注意力机制，在每个时间点对相关多模态上下文（图像数据、时间不变数据等）进行对齐。长短期记忆（LSTM）模型也被用作基线模型。使用了两个不同的数据集：一个来自开源成像联盟（OSIC）的数据集，包含人口统计数据和肺功能的结构化时间序列数据以及肺部基线CT扫描；另一个来自阿尔茨海默病神经影像学倡议（ADNI）的数据集，包含一系列MRI扫描以及人口统计数据、体格检查和认知评估数据。进行了消融研究以了解CDEs、多模态数据、注意力融合和插值策略对模型性能的作用。当评估基线模型时，使用多模态数据提高了神经CDE的性能，测试RMSE更低。此外，多模态神经CDE的性能也优于多模态LSTM。在基于注意力的架构中，通过拼接和矩形插值进行融合被发现可以提高模型性能。所提出的RealDiffFusionNet的性能被发现优于所有模型（0.2570）。对于ADNI数据集，在仅在结构化数据上训练的神经CDE和LSTM模型之间，测试RMSE是相当的（LSTM为0.471，神经CDE为0.4581）。此外，添加患者MRI序列中的图像特征提高了性能，测试RMSE更低（多模态为0.4372，结构化数据为0.4581）。RealDiffFusionNet在利用CDEs和多模态数据准确预测疾病进展方面显示出前景。 显示英文摘要

摘要： This paper presents a novel deep learning-based approach named RealDiffFusionNet incorporating Neural Controlled Differential Equations (Neural CDE) - time series models that are robust in handling irregularly sampled data - and multi-head attention to align relevant multimodal context (image data, time invariant data, etc.) at each time point. Long short-term memory (LSTM) models were also used as a baseline. Two different datasets were used: a data from the Open-Source Imaging Consortium (OSIC) containing structured time series data of demographics and lung function with a baseline CT scan of the lungs and the second from the Alzheimer's Disease Neuroimaging Initiative (ADNI) containing a series of MRI scans along with demographics, physical examinations, and cognitive assessment data. An ablation study was performed to understand the role of CDEs, multimodal data, attention fusion, and interpolation strategies on model performance. When the baseline models were evaluated, the use of multimodal data resulted in an improvement in Neural CDE performance, with a lower test RMSE. Additionally, the performance of multimodal Neural CDE was also superior to multimodal LSTM. In the attention-based architectures, fusion through concatenation and rectilinear interpolation were found to improve model performance. The performance of the proposed RealDiffFusionNet was found to be superior (0.2570) to all models. For the ADNI dataset, between the Neural-CDE and LSTM models trained only on the structured data, the test RMSE were comparable (0.471 for LSTM vs. 0.4581 Neural-CDE). Furthermore, the addition of image features from patients' MRI series resulted in an improvement in performance, with a lower test RMSE (0.4372 with multimodal vs 0.4581 with structured data). RealDiffFusionNet has shown promise in utilizing CDEs and multimodal data to accurately predict disease progression.