Clinical notes are an essential component of a health record. This paper
evaluates how natural language processing (NLP) can be used to identify the
risk of acute care use (ACU) in oncology patients, once chemotherapy starts.
Risk prediction using structured health data (SHD) is now standard, but
predictions using free-text formats are complex. This paper explores the use of
free-text notes for the prediction of ACU instead of SHD. Deep Learning models
were compared to manually engineered language features. Results show that SHD
models minimally outperform NLP models; an l1-penalised logistic regression
with SHD achieved a C-statistic of 0.748 (95%-CI: 0.735, 0.762), while the same
model with language features achieved 0.730 (95%-CI: 0.717, 0.745) and a
transformer-based model achieved 0.702 (95%-CI: 0.688, 0.717). This paper shows
how language models can be used in clinical applications and underlines how
risk bias is different for diverse patient groups, even using only free-text
data.

本研究使用自然语言处理技术，比较了深度学习模型和手动工程化语言特征模型，在使用自由文本格式预测急性护理使用风险上的表现，结果表明，使用结构化健康数据模型能够最小限度地胜过 NLP 模型。