Review
Version 1
Preserved in Portico This version is not peer-reviewed
Modern Clinical Text Mining: A Guide and Review
Bethany Percha
Version 1
: Received: 29 October 2020 / Approved: 30 October 2020 / Online: 30 October 2020 (15:01:24 CET)
Version 2 : Received: 2 February 2021 / Approved: 3 February 2021 / Online: 3 February 2021 (10:31:14 CET)
Version 2 : Received: 2 February 2021 / Approved: 3 February 2021 / Online: 3 February 2021 (10:31:14 CET)
A peer-reviewed article of this Preprint also exists.
Journal reference: Annual Review of Biomedical Data Science 2021, 4, 165-187
DOI: 10.1146/annurev-biodatasci-030421-030931
Abstract
Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.
Subject Areas
text mining; natural language processing; electronic health records; clinical text; machine learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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