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

ABSTRACT:

Background

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.

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 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 emb

SUBMITTER: Wu H 

PROVIDER: S-EPMC6938594 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature