Paper: Under review

Foresight v2 (FS2) is a Large Language Model fine-tuned on hospital data for modelling patient timelines. It is capable of understanding patient's clinical notes and predicting SNOMED codes for a wide range of biomedical use cases including diagnosis suggestion, risk forecasting, and procedure and medication recommendation. FS2 is trained on the free text portion of the MIMIC-III dataset, firstly through the extraction of biomedical concepts and then the creation of contextualised patient timelines, upon which the model is then fine-tuned.

The results show significant improvement over the previous state-of-the-art for the next new biomedical concept prediction (P/R - 0.73/0.66 vs 0.52/0.32) and a similar improvement specifically for the next new disorder prediction (P/R - 0.69/0.62 vs 0.46/0.25). Finally, on the task of risk forecast, we compare our model, to GPT-4-turbo, and show that FS2 performs significantly better on such tasks (P@5 - 0.90 vs 0.65). This highlights the need to incorporate hospital data into LLMs and shows that small models when fine-tuned on high-quality specialised data outperform much larger ones.