Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment^1,2,3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing^4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7–94.9%, with an improvement of 5.36–14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

医生每天都会在时间有限的情况下做出关键的决定。临床预测模型可以通过预测临床和操作事件来帮助医生和管理人员做出决策。由于数据处理以及模型开发和部署的复杂性，现有的基于结构化数据的临床预测模型在日常实践中的使用有限^1,2,3。在这里，我们展示了电子健康记录中的非结构化临床记录可以实现临床语言模型的训练，该模型可以用作具有低阻力开发和部署的通用临床预测引擎。我们的方法利用自然语言处理^4,5的最新进展来训练医学语言 (NYUTron) 的大型语言模型，然后在广泛的临床和操作预测任务中对其进行微调。我们评估了我们的卫生系统中针对五项此类任务的方法：30 天全因再入院预测、住院死亡率预测、合并症指数预测、住院时间预测和保险拒绝预测。我们发现 NYUTron 的曲线下面积 (AUC) 为 78.7-94.9%，与传统模型相比，AUC 提高了 5.36-14.7%。我们还展示了使用临床文本进行预训练的好处，通过微调和在前瞻性单臂试验中全面部署我们的系统来提高不同站点的通用性的潜力。这些结果显示了在医学中使用临床语言模型与医生一起阅读并在护理点提供指导的潜力。