Dive deeper into the aspects covered in this lesson with these readings that expand on the topics discussed. Each reading includes reflective questions to help you focus on key learning points and strengthen your understanding.

Reading: Health system-scale language models are all-purpose prediction engines.
By Jiang L.Y. et al.

In the talk with Prof. Levi, we've touched on how Large Language Models can leverage unstructured clinical notes in Electronic Health Records (EHRs). This paper delves deeper into how we can transform this unstructured data into powerful prediction tools. By generating forecasts based on comprehensive patient data, these models can help healthcare professionals make more informed decisions.

Reading time: ~30 min

   Download the reading

Reflective questions

  • For which tasls were the Large Language Models employed, as reported by the authors?
  • Were unstructured text data sufficient to achieve all the clinical and operational outcomes?

Source: Jiang, L.Y., Liu, X.C., Nejatian, N.P. et al. Health system-scale language models are all-purpose prediction engines. Nature 619, 357–362 (2023).  Link