TRIALSCOPE A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models. (arXiv:2311.01301v1 [cs.LG])

The rapid digitization of real-world data offers an unprecedented opportunity
for optimizing healthcare delivery and accelerating biomedical discovery. In
practice, however, such data is most abundantly available in unstructured
forms, such as clinical notes in electronic medical records (EMRs), and it is
generally plagued by confounders. In this paper, we present TRIALSCOPE, a
unifying framework for distilling real-world evidence from population-level
observational data. TRIALSCOPE leverages biomedical language models to
structure clinical text at scale, employs advanced probabilistic modeling for
denoising …

arxiv biomedical clinical data delivery digitization discovery electronic evidence framework healthcare language language models medical medical records notes practice records scaling unstructured world