  Electronic health records (EHRs) hold significant value for research and
applications. As a new way of information extraction, question answering (QA)
can extract more flexible information than conventional methods and is more
accessible to clinical researchers, but its progress is impeded by the scarcity
of annotated data. In this paper, we propose a novel approach that
automatically generates training data for transfer learning of QA models. Our
pipeline incorporates a preprocessing module to handle challenges posed by
extraction types that are not readily compatible with extractive QA frameworks,
including cases with discontinuous answers and many-to-one relationships. The
obtained QA model exhibits excellent performance on subtasks of information
extraction in EHRs, and it can effectively handle few-shot or zero-shot
settings involving yes-no questions. Case studies and ablation studies
demonstrate the necessity of each component in our design, and the resulting
model is deemed suitable for practical use.
