Neural networks (NNs) have become the state of the art in many machine
learning applications, especially in image and sound processing [1]. The same,
although to a lesser extent [2,3], could be said in natural language processing
(NLP) tasks, such as named entity recognition. However, the success of NNs
remains dependent on the availability of large labelled datasets, which is a
significant hurdle in many important applications. One such case are electronic
health records (EHRs), which are arguably the largest source of medical data,
most of which lies hidden in natural text [4,5]. Data access is difficult due
to data privacy concerns, and therefore annotated datasets are scarce. With
scarce data, NNs will likely not be able to extract this hidden information
with practical accuracy. In our study, we develop an approach that solves these
problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009
Medical Extraction Challenge [6], 4.3 above the architecture that won the
competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on
extracting relationships between medical terms. To reach this state-of-the-art
accuracy, our approach applies transfer learning to leverage on datasets
annotated for other I2B2 tasks, and designs and trains embeddings that
specially benefit from such transfer.Comment: 11 pages, 4 figures, 8 table
