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JMIR Med Inform. 2019 Dec 17;7(4):e14782. doi: 10.2196/14782.

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach.

Author information

Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China.
Health Data Research UK, University of Edinburgh, Edinburgh, United Kingdom.
Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
South London and Maudsley NHS Foundation Trust, London, United Kingdom.
Centre for Epidemiology and Biostatistics, Melbourne School of Global and Population Health, The University of Melbourne, Melbourne, Australia.
Health Data Research UK, University College London, London, United Kingdom.



Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results.


The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records.


We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste," which collectively impede efficient model reuse. We propose a phenotype embedding-based approach to minimize these sources of waste without the need for labelled data from new settings.


We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in "blind" model-adaptation approaches.


Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.


clustering; electronic health records; machine learning; model adaptation; natural language processing; phenotype; phenotype embedding; text mining; word embedding

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