  Background: Many efforts have been put into the use of automated approaches,
such as natural language processing (NLP), to mine or extract data from
free-text medical records to construct comprehensive patient profiles for
delivering better health-care. Reusing NLP models in new settings, however,
remains cumbersome - requiring validation and/or retraining on new data
iteratively to achieve convergent results.
  Objective: The aim of this work is to minimize the effort involved in reusing
NLP models on free-text medical records.
  Methods: We formally define and analyse 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.
  Results: 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% (duplicate waste), i.e. 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% (imbalance waste), i.e.
the effort required in "blind" model-adaptation approaches.
  Conclusions: Adapting pre-trained 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.
