In recent years, estimating the duration of medical intervention based on
electronic health records (EHRs) has gained significant attention in the filed
of clinical decision support. However, current models largely focus on
structured data, leaving out information from the unstructured clinical
free-text data. To address this, we present a novel language-enhanced
transformer-based framework, which projects all relevant clinical data
modalities (continuous, categorical, binary, and free-text features) into a
harmonized language latent space using a pre-trained sentence encoder with the
help of medical prompts. The proposed method enables the integration of
information from different modalities within the cell transformer encoder and
leads to more accurate duration estimation for medical intervention. Our
experimental results on both US-based (length of stay in ICU estimation) and
Asian (surgical duration prediction) medical datasets demonstrate the
effectiveness of our proposed framework, which outperforms tailored baseline
approaches and exhibits robustness to data corruption in EHRs.

提出了一种语言增强的基于转换器的框架，用于在连续、分类、二进制和自由文本功能之间进行信息集成，以更准确地估计医学干预的持续时间，并在 US 和 Asian 的数据集上证明了其有效性。