MIMIC-III-Ext-tPatchGNN

This dataset is a curated subset of MIMIC-III (v1.4), specifically formatted to facilitate reproducibility of the experiments in the work t-PatchGNN. It serves as part of a benchmark designed for forecasting irregular multivariate clinical time series, that is, given a set of historical Irregular Multivariate Time Series (IMTS) observations and forecasting queries, the forecasting problem aims to accurately forecast the values in correspondence to these queries. This requires addressing key challenges such as missing data, variable sampling rates, and complex temporal dependencies. The dataset includes patient records with diverse physiological measurements, each sampled at irregular intervals, reflecting real-world clinical scenarios. It is structured to capture both short-term and long-term temporal patterns, making it well-suited for evaluating machine learning models in medical time series forecasting. By providing a standardized benchmark, this dataset aims to advance research in predictive modeling for healthcare, enabling the development of robust algorithms that can handle irregular and sparse clinical data. The dataset's applications extend to critical areas such as early disease detection, patient risk stratification, and treatment outcome prediction, making it a valuable resource for the medical AI and machine learning communities.本数据集为MIMIC-III（v1.4）的精选子集，经过专门格式化处理，以复现t-PatchGNN相关研究中的实验流程。本数据集属于面向不规则多变量临床时间序列预测的基准测试集之一：给定一组历史不规则多变量时间序列（Irregular Multivariate Time Series, IMTS）观测数据与预测查询任务，该预测任务的核心目标是精准预测对应查询点的数值。此类任务需解决缺失数据、采样率不均及复杂时间依赖关系等核心挑战。数据集包含涵盖多种生理测量指标的患者记录，各项指标均以不规则时间间隔采样，贴合真实临床场景的实际情况。该数据集的结构设计兼顾短期与长期时间模式的捕捉，非常适合用于评估医疗时间序列预测领域的机器学习模型。通过提供标准化的基准测试集，本数据集旨在推动医疗预测建模领域的研究发展，助力开发能够处理不规则且稀疏临床数据的鲁棒算法。该数据集的应用场景涵盖疾病早期检测、患者风险分层及治疗结局预测等关键领域，是医疗人工智能与机器学习研究社区的宝贵资源。