pyAKI——一种用于自动化KDIGO分类的开源解决方案
pyAKI -- An Open Source Solution to Automated KDIGO classification
AI总结：
针对缺乏标准化开源工具的问题，研究推出开源流水线pyAKI，基于MIMIC-IV数据库子集开发验证，通过标准化数据模型确保可重复性，其KDIGO标准实施性能稳健且质量超越人工标注。
摘要：
急性肾损伤（AKI）是重症患者的常见并发症，重症监护病房中多达50%的患者受其影响。缺乏将肾脏病改善全球预后（KDIGO）标准应用于时间序列数据的标准化开源工具，会对工作量和研究质量产生负面影响。本项目推出pyAKI，这是一个开源流水线，通过提供一致实施KDIGO标准的综合解决方案来填补这一空白。pyAKI流水线是利用重症监护医学信息库（MIMIC）-IV数据库的一个子集开发并验证的，该数据库是重症监护研究中常用的数据库。我们定义了一个标准化数据模型以确保可重复性。与专家标注的对比验证表明，pyAKI在实施KDIGO标准方面性能稳健。对比分析显示，其质量能够超越人工标注。本研究推出的pyAKI是一种用于实施AKI诊断KDIGO标准的开源解决方案，可基于时间序列数据实现高准确率与高性能。
英文摘要：
Acute Kidney Injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series data has a negative impact on workload and study quality. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation. The pyAKI pipeline was developed and validated using a subset of the Medical Information Mart for Intensive Care (MIMIC)-IV database, a commonly used database in critical care research. We defined a standardized data model in order to ensure reproducibility. Validation against expert annotations demonstrated pyAKI's robust performance in implementing KDIGO criteria. Comparative analysis revealed its ability to surpass the quality of human labels. This work introduces pyAKI as an open-source solution for implementing the KDIGO criteria for AKI diagnosis using time series data with high accuracy and performance.
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。
