Adapting State-of-the-Art Deep Language Models to Clinical Information Extraction Systems: Potentials, Challenges, and Solutions.

ABSTRACT:

Background

Deep learning (DL) has been widely used to solve problems with success in speech recognition, visual object recognition, and object detection for drug discovery and genomics. Natural language processing has achieved noticeable progress in artificial intelligence. This gives an opportunity to improve on the accuracy and human-computer interaction of clinical informatics. However, due to difference of vocabularies and context between a clinical environment and generic English, transplanting language models directly from up-to-date methods to real-world health care settings is not always satisfactory. Moreover, the legal restriction on using privacy-sensitive patient records hinders the progress in applying machine learning (ML) to clinical language processing.

Objective

SUBMITTER: Zhou L 

PROVIDER: S-EPMC6658232 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature