  Medical information extraction consists of a group of natural language
processing (NLP) tasks, which collaboratively convert clinical text to
pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are
highly integrated with deep learning techniques and thus require massive
annotated linguistic data. This study presents an engineering framework of
medical entity recognition, relation extraction and attribute extraction, which
are unified in annotation, modeling and evaluation. Specifically, the
annotation scheme is comprehensive, and compatible between tasks, especially
for the medical relations. The resulted annotated corpus includes 1,200 full
medical records (or 18,039 broken-down documents), and achieves inter-annotator
agreements (IAAs) of 94.53%, 73.73% and 91.98% F 1 scores for the three tasks.
Three task-specific neural network models are developed within a shared
structure, and enhanced by SOTA NLP techniques, i.e., pre-trained language
models. Experimental results show that the system can retrieve medical
entities, relations and attributes with F 1 scores of 93.47%, 67.14% and
90.89%, respectively. This study, in addition to our publicly released
annotation scheme and code, provides solid and practical engineering experience
of developing an integrated medical information extraction system.
