Fine-tuning pre-trained extractive QA models for clinical document
  parsing
Ashwyn Sharma David I. Feldman Aneesh Jain
Electronic health records (EHRs) contain a vast amount of high-dimensional
multi-modal data that can accurately represent a patient's medical history.
Unfortunately, most of this data is either unstructured or semi-structured,
rendering it unsuitable for real-time and retrospective analyses. A remote
patient monitoring (RPM) program for Heart Failure (HF) patients needs to have
access to clinical markers like EF (Ejection Fraction) or LVEF (Left
Ventricular Ejection Fraction) in order to ascertain eligibility and
appropriateness for the program. This paper explains a system that can parse
echocardiogram reports and verify EF values. This system helps identify
eligible HF patients who can be enrolled in such a program. At the heart of
this system is a pre-trained extractive QA transformer model that is fine-tuned
on custom-labeled data. The methods used to prepare such a model for deployment
are illustrated by running experiments on a public clinical dataset like
MIMIC-IV-Note. The pipeline can be used to generalize solutions to similar
problems in a low-resource setting. We found that the system saved over 1500
hours for our clinicians over 12 months by automating the task at scale.