TransConv-DDPM：增强的扩散模型用于生成医疗领域的时序数据
TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare
AI总结：
TransConv-DDPM通过结合DDPM、U-Net和Transformer层，有效生成医疗领域的高质量时序数据，提升预测模型性能。
摘要：
临床领域缺乏真实世界数据在训练有效的AI模型以用于诊断和预防工具方面构成了主要障碍。生成式AI在增加数据量和增强模型训练方面显示出潜力，特别是在计算机视觉和自然语言处理（NLP）领域。然而，生成生理时序数据，这是医疗AI应用中常见的一种类型，由于其内在的复杂性和变异性，呈现出独特的挑战。本文介绍TransConv-DDPM，一种增强的生成式AI方法，用于生物力学和生理时序数据的生成。该模型采用去噪扩散概率模型（DDPM）与U-Net、多尺度卷积模块和变压器层相结合，以捕捉全局和局部的时间依赖性。我们评估了TransConv-DDPM在三个多样化的数据集上，生成长和短序列的时序数据。通过使用四个性能指标对最先进的方法TimeGAN和Diffusion-TS进行定量比较，展示了有希望的结果，特别是在SmartFallMM和EEG数据集上，它有效捕捉了数据点之间的更渐进的时间变化模式。此外，在SmartFallMM数据集上的实用性测试中，发现添加由TransConv-DDPM生成的合成跌倒数据可以提高预测模型的性能，与仅在SmartFallMM数据集上训练的基线模型相比，F1分数提高了13.64%，整体准确率增加了14.93%。这些发现突显了TransConv-DDPM在生成高质量合成数据用于实际应用方面的潜力。
英文摘要：
The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promise in increasing data volume and enhancing model training, particularly in computer vision and natural language processing (NLP) domains. However, generating physiological time-series data, a common type in medical AI applications, presents unique challenges due to its inherent complexity and variability. This paper introduces TransConv-DDPM, an enhanced generative AI method for biomechanical and physiological time-series data generation. The model employs a denoising diffusion probabilistic model (DDPM) with U-Net, multi-scale convolution modules, and a transformer layer to capture both global and local temporal dependencies. We evaluated TransConv-DDPM on three diverse datasets, generating both long and short-sequence time-series data. Quantitative comparisons against state-of-the-art methods, TimeGAN and Diffusion-TS, using four performance metrics, demonstrated promising results, particularly on the SmartFallMM and EEG datasets, where it effectively captured the more gradual temporal change patterns between data points. Additionally, a utility test on the SmartFallMM dataset revealed that adding synthetic fall data generated by TransConv-DDPM improved predictive model performance, showing a 13.64% improvement in F1-score and a 14.93% increase in overall accuracy compared to the baseline model trained solely on fall data from the SmartFallMM dataset. These findings highlight the potential of TransConv-DDPM to generate high-quality synthetic data for real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
