Clinical Risk Prediction Using Language Models: Benefits And
  Considerations
Angeela Acharya Sulabh Shrestha Anyi Chen Joseph Conte Sanja Avramovic Siddhartha Sikdar Antonios Anastasopoulos Sanmay Das
The utilization of Electronic Health Records (EHRs) for clinical risk
prediction is on the rise. However, strict privacy regulations limit access to
comprehensive health records, making it challenging to apply standard machine
learning algorithms in practical real-world scenarios. Previous research has
addressed this data limitation by incorporating medical ontologies and
employing transfer learning methods. In this study, we investigate the
potential of leveraging language models (LMs) as a means to incorporate
supplementary domain knowledge for improving the performance of various
EHR-based risk prediction tasks. Unlike applying LMs to unstructured EHR data
such as clinical notes, this study focuses on using textual descriptions within
structured EHR to make predictions exclusively based on that information. We
extensively compare against previous approaches across various data types and
sizes. We find that employing LMs to represent structured EHRs, such as
diagnostic histories, leads to improved or at least comparable performance in
diverse risk prediction tasks. Furthermore, LM-based approaches offer numerous
advantages, including few-shot learning, the capability to handle previously
unseen medical concepts, and adaptability to various medical vocabularies.
Nevertheless, we underscore, through various experiments, the importance of
being cautious when employing such models, as concerns regarding the
reliability of LMs persist.