Multimodal BEHRT: transformers for multimodal electronic health records to predict breast cancer prognosis.

ABSTRACT:

Background

Electronic Health Records (EHRs) contain a wealth of information about patients that could be useful toward improving treatment outcomes for breast cancer patients, but remain mostly unexploited. Recent methodological developments in deep learning, however, open the way to developing new methods to leverage this information to improve patient care.

Methods

We propose M-BEHRT, a Multimodal BERT for EHR data based on BEHRT, itself an architecture based on the popular natural language architecture BERT (Bidirectional Encoder Representations from Transformers). M-BEHRT models multimodal patient trajectories as a sequence of medical visits, comprising a variety of information such as clinical features, results from biological lab tests, medical department and procedure

SUBMITTER: Mbaye NM 

PROVIDER: S-EPMC12575146 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature