Literature DB >> 31259026

Assessing the Readability of Freely Available ICU Notes.

Wu-Chen Su1, Kevin Dufendach1,2,3, Danny T Y Wu1,3.   

Abstract

Unstructured data stored in an electronic health record (EHR) system can be very informative but require techniques such as natural language processing to extract the information. Developing such techniques requires shared data, but clinical data are often not easy to access. A freely available intensive care unit database, MIMIC-III, was released in 2016 to address this issue and benefit the informatics research community. While the database has been utilized by a few studies, the text characteristics of the notes have not been summarized. In this study, we present the summary of the basic text characteristics and the readability of the MIMIC-III ICU notes. We further compare the results with our previous study where proprietary EHR notes were used. The results show that the text characteristics of MIMIC-III notes were comparable with proprietary EHR notes, although the note readability index was slightly lower. The clinical notes in MIMIC-III can be a viable option for researchers who are interested in clinicians' language use but have no access to proprietary EHR systems.

Year:  2019        PMID: 31259026      PMCID: PMC6568110     

Source DB:  PubMed          Journal:  AMIA Jt Summits Transl Sci Proc


  1 in total

1.  DI++: A deep learning system for patient condition identification in clinical notes.

Authors:  Jinhe Shi; Xiangyu Gao; William C Kinsman; Chenyu Ha; Guodong Gordon Gao; Yi Chen
Journal:  Artif Intell Med       Date:  2021-12-02       Impact factor: 5.326

  1 in total

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