Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking.

ABSTRACT:

Objective

Normalizing clinical mentions to concepts in standardized medical terminologies, in general, is challenging due to the complexity and variety of the terms in narrative medical records. In this article, we introduce our work on a clinical natural language processing (NLP) system to automatically normalize clinical mentions to concept unique identifier in the Unified Medical Language System. This work was part of the 2019 n2c2 (National NLP Clinical Challenges) Shared-Task and Workshop on Clinical Concept Normalization.

Materials and methods

We developed a hybrid clinical NLP system that combines a generic multilevel matching framework, customizable matching components, and machine learning ranking systems. We explored 2 machine leaning ranking systems based on eithe

SUBMITTER: Chen L 

PROVIDER: S-EPMC7647369 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature