Electronic health record systems are ubiquitous and the majority of patients’ data are now being collected electronically in the form of free text. Deep learning has significantly advanced the field of natural language processing and the self-supervised representation learning and the transfer learning have become the methods of choice in particular when the high quality annotated data are limited. 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 (NER) model for clinical natural language processing. The model is trained to recognise seven categories: drug names, route of administration, frequency, dosage, strength, form, duration. The model was first pre-trained on the task of predicting the next word, using a collection of 2 million free-text patients’ records from MIMIC-III corpora followed by fine-tuning on the named-entity recognition task. The model achieved a micro-averaged F1 score of 0.957 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 the target domain data in order to achieve more accurate results. The resulting model and the pre-trained embeddings are available at https://github.com/kormilitzin/med7.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)