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Functioning is gaining recognition as an important indicator of globalhealth, but remains under-studied in medical natural language processingresearch. We present the first analysis of automatically extractingdescriptions of patient mobility, using a recently-developed dataset of freetext electronic health records. We frame the task as a named entity recognition(NER) problem, and investigate the applicability of NER techniques to mobilityextraction. As text corpora focused on patient functioning are scarce, weexplore domain adaptation of word embeddings for use in a recurrent neuralnetwork NER system. We find that embeddings trained on a small in-domain corpusperform nearly as well as those learned from large out-of-domain corpora, andthat domain adaptation techniques yield additional improvements in bothprecision and recall. Our analysis identifies several significant challenges inextracting descriptions of patient mobility, including the length andcomplexity of annotated entities and high linguistic variability in mobilitydescriptions.

Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility