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)
Percha, B. Modern Clinical Text Mining: A Guide and Review. Annual Review of Biomedical Data Science, 2021, 4, 165–187. https://doi.org/10.1146/annurev-biodatasci-030421-030931.
Percha, B. Modern Clinical Text Mining: A Guide and Review. Annual Review of Biomedical Data Science, 2021, 4, 165–187. https://doi.org/10.1146/annurev-biodatasci-030421-030931.
Percha, B. Modern Clinical Text Mining: A Guide and Review. Annual Review of Biomedical Data Science, 2021, 4, 165–187. https://doi.org/10.1146/annurev-biodatasci-030421-030931.
Percha, B. Modern Clinical Text Mining: A Guide and Review. Annual Review of Biomedical Data Science, 2021, 4, 165–187. https://doi.org/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.
Keywords
text mining; natural language processing; electronic health records; clinical text; machine learning
Subject
Computer Science and Mathematics, Information Systems
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.
Received:
3 February 2021
Commenter:
Bethany Percha
Commenter's Conflict of Interests:
Author
Comment:
This version incorporates suggested changes from reviewers and my own revisions. Most of the changes are in Sections 2 and 3 and the Conclusion.
Comment:
Update: a potentially important software resource for clinical text mining that did not make it into Table 1 of the manuscript is medspaCy https://spacy.io/universe/project/medspacy.
Commenter: Bethany Percha
Commenter's Conflict of Interests: Author
Commenter:
Commenter's Conflict of Interests: Author