We introduce RatchetEHR, a novel transformer-based framework designed for the
predictive analysis of electronic health records (EHR) data in intensive care
unit (ICU) settings, with a specific focus on bloodstream infection (BSI)
prediction. Leveraging the MIMIC-IV dataset, RatchetEHR demonstrates superior
predictive performance compared to other methods, including RNN, LSTM, and
XGBoost, particularly due to its advanced handling of sequential and temporal
EHR data. A key innovation in RatchetEHR is the integration of the Graph
Convolutional Transformer (GCT) component, which significantly enhances the
ability to identify hidden structural relationships within EHR data, resulting
in more accurate clinical predictions. Through SHAP value analysis, we provide
insights into influential features for BSI prediction. RatchetEHR integrates
multiple advancements in deep learning which together provide accurate
predictions even with a relatively small sample size and highly imbalanced
dataset. This study contributes to medical informatics by showcasing the
application of advanced AI techniques in healthcare and sets a foundation for
further research to optimize these capabilities in EHR data analysis.

RatchetEHR 是一种基于 transformer 的框架，旨在在重症监护室环境中对电子健康记录（EHR）数据进行预测性分析，特别关注血液感染（BSI）预测。通过整合图卷积转换器（GCT）组件，RatchetEHR 能够识别 EHR 数据中隐藏的结构关系，从而提供更准确的临床预测。该研究展示了先进的人工智能技术在医疗保健中的应用，并为优化 EHR 数据分析的能力奠定了基础。