A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing

TL;DR

This paper reviews publicly available clinical NLP tasks using electronic health records, highlighting growth, challenges, and future directions for better collaboration and standardization.

Contribution

It provides a comprehensive scoping review of 35 papers on clinical NLP tasks with publicly available data, identifying gaps and proposing future research directions.

Findings

Growing diversity of clinical NLP tasks over time

Identified gaps in data generalizability and standardization

Highlighted need for multidisciplinary collaboration

Abstract

Objective: to provide a scoping review of papers on clinical natural language processing (NLP) tasks that use publicly available electronic health record data from a cohort of patients. Materials and Methods: We searched six databases, including biomedical research and computer science literature database. A round of title/abstract screening and full-text screening were conducted by two reviewers. Our method followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Results: A total of 35 papers with 47 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including name entity recognition, summarization, and other NLP tasks. Some tasks were introduced with a topic of clinical decision support applications, such as substance abuse, phenotyping, cohort selection for clinical trial. We summarized the tasks by publication and dataset information. Discussion: The breadth of clinical NLP tasks keeps growing as the field of NLP evolves with advancements in language systems. However, gaps exist in divergent interests between general domain NLP community and clinical informatics community, and in generalizability of the data sources. We also identified issues in data selection and preparation including the lack of time-sensitive data, and invalidity of problem size and evaluation. Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multi-disciplinary collaboration, reporting transparency, and standardization in data preparation.