Electronic Health Records (EHRs) are rich sources of patient-level data,
including laboratory tests, medications, and diagnoses, offering valuable
resources for medical data analysis. However, concerns about privacy often
restrict access to EHRs, hindering downstream analysis. Researchers have
explored various methods for generating privacy-preserving EHR data. In this
study, we introduce a new method for generating diverse and realistic synthetic
EHR time series data using Denoising Diffusion Probabilistic Models (DDPM). We
conducted experiments on six datasets, comparing our proposed method with seven
existing methods. Our results demonstrate that our approach significantly
outperforms all existing methods in terms of data utility while requiring less
training effort. Our approach also enhances downstream medical data analysis by
providing diverse and realistic synthetic EHR data.

使用去噪扩散概率模型（DDPM）生成多样且逼真的合成电子健康记录时间序列数据，提高数据效用且减少训练工作量，从而改善医疗数据分析。

电子病历时间序列的快速可靠生成方法

Fast and Reliable Generation of EHR Time Series via Diffusion Models

Computer vision technology is widely used in biological and medical data
analysis and understanding. However, there are still two major bottlenecks in
the field of cell membrane segmentation, which seriously hinder further
research: lack of sufficient high-quality data and lack of suitable evaluation
criteria. In order to solve these two problems, this paper first proposes an
Ultra-high Resolution Image Segmentation dataset for the Cell membrane, called
U-RISC, the largest annotated Electron Microscopy (EM) dataset for the Cell
membrane with multiple iterative annotations and uncompressed high-resolution
raw data. During the analysis process of the U-RISC, we found that the current
popular segmentation evaluation criteria are inconsistent with human
perception. This interesting phenomenon is confirmed by a subjective experiment
involving twenty people. Furthermore, to resolve this inconsistency, we propose
a new evaluation criterion called Perceptual Hausdorff Distance (PHD) to
measure the quality of cell membrane segmentation results. Detailed performance
comparison and discussion of classic segmentation methods along with two
iterative manual annotation results under existing evaluation criteria and PHD
is given.

本文提出了一个用于细胞膜分割的电子显微数据集 U-RISC，该数据集具有多次迭代注释和未压缩的高分辨率原始数据。针对当前标准分割评估方法与人类视觉认知差异的问题，本文提出了一种称为感知豪斯多夫距离（PHD）的新评估标准，并对该标准与现有标准的性能进行了详细的比较和讨论。