Natural language processing in the intensive care unit: A scoping review

Abstract

Objectives

Natural language processing (NLP) is a branch of artificial intelligence focused on enabling computers to interpret and analyse text-based data. The intensive care specialty is known to generate large volumes of data, including free-text, however, NLP applications are not commonly used either in critical care clinical research or quality improvement projects. This review aims to provide an overview of how NLP has been used in the intensive care specialty and promote an understanding of NLP's potential future clinical applications.

Design

Scoping review.

Data sources

A systematic search was developed with an information specialist and deployed on the PubMed electronic journal database. Results were restricted to the last 10 years to ensure currency.

Review methods

Screening and data extraction were undertaken by two independent reviewers, with any disagreements resolved by a third. Given the heterogeneity of the eligible articles, a narrative synthesis was conducted.

Results

Eighty-seven eligible articles were included in the review. The most common type (n = 24) were studies that used NLP-derived features to predict clinical outcomes, most commonly mortality (n = 16). Next were articles that used NLP to identify a specific concept (n = 23), including sepsis, family visitation and mental health disorders. Most studies only described the development and internal validation of their algorithm (n = 79), and only one reported the implementation of an algorithm in a clinical setting.

Conclusions

Natural language processing has been used for a variety of purposes in the ICU context. Increasing awareness of these techniques amongst clinicians may lead to more clinically relevant algorithms being developed and implemented.