  Electronic health records contain an enormous amount of valuable information,
but many are recorded in free text. Information extraction is the strategy to
transform the sequence of characters into structured data, which can be
employed for secondary analysis. However, the traditional information
extraction components, such as named entity recognition and relation
extraction, require annotated data to optimize the model parameters, which has
become one of the major bottlenecks in building information extraction systems.
With the large language models achieving good performances on various
downstream NLP tasks without parameter tuning, it becomes possible to use large
language models for zero-shot information extraction. In this study, we aim to
explore whether the most popular large language model, ChatGPT, can extract
useful information from the radiological reports. We first design the prompt
template for the interested information in the CT reports. Then, we generate
the prompts by combining the prompt template with the CT reports as the inputs
of ChatGPT to obtain the responses. A post-processing module is developed to
transform the responses into structured extraction results. We conducted the
experiments with 847 CT reports collected from Peking University Cancer
Hospital. The experimental results indicate that ChatGPT can achieve
competitive performances for some extraction tasks compared with the baseline
information extraction system, but some limitations need to be further
improved.
