Work

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

Year: 2018

Type: conference-paper

Abstract: Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research.We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records.We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction.As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system.We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-ofdomain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall.Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.

DOI: 10.18653/v1/w18-2301

Authors: Denis Newman-Griffis, Ayah Zirikly

Institutions: National Institutes of Health, National Institutes of Health Clinical Center, The Ohio State University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Funders: U.S. Social Security Administration, National Institutes of Health

OpenAlex ID: W2807141747