| Summary: | The field of clinical natural language processing has been advanced
significantly since the introduction of deep learning models. The
self-supervised representation learning and the transfer learning paradigm
became the methods of choice in many natural language processing application,
in particular in the settings with the dearth of high quality manually
annotated data. Electronic health record systems are ubiquitous and the
majority of patients' data are now being collected electronically and in
particular in the form of free text. Identification of medical concepts and
information extraction is a challenging task, yet important ingredient for
parsing unstructured data into structured and tabulated format for downstream
analytical tasks. In this work we introduced a named-entity recognition model
for clinical natural language processing. The model is trained to recognise
seven categories: drug names, route, frequency, dosage, strength, form,
duration. The model was first self-supervisedly pre-trained by predicting the
next word, using a collection of 2 million free-text patients' records from
MIMIC-III corpora and then fine-tuned on the named-entity recognition task. The
model achieved a lenient (strict) micro-averaged F1 score of 0.957 (0.893)
across all seven categories. Additionally, we evaluated the transferability of
the developed model using the data from the Intensive Care Unit in the US to
secondary care mental health records (CRIS) in the UK. A direct application of
the trained NER model to CRIS data resulted in reduced performance of F1=0.762,
however after fine-tuning on a small sample from CRIS, the model achieved a
reasonable performance of F1=0.944. This demonstrated that despite a close
similarity between the data sets and the NER tasks, it is essential to
fine-tune on the target domain data in order to achieve more accurate results.
|
|---|