Computer-assisted de-identification of free-text nursing notes

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.

Bibliographic Details
Main Author: Douglass, Margaret, 1981-
Other Authors: Roger G. Mark.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2006
Subjects:
Online Access:http://hdl.handle.net/1721.1/33299
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author Douglass, Margaret, 1981-
author2 Roger G. Mark.
author_facet Roger G. Mark.
Douglass, Margaret, 1981-
author_sort Douglass, Margaret, 1981-
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description Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
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spelling mit-1721.1/332992019-04-11T03:13:27Z Computer-assisted de-identification of free-text nursing notes Douglass, Margaret, 1981- Roger G. Mark. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. Includes bibliographical references (leaves 67-70). Medical researchers are legally required to protect patients' privacy by removing personally identifiable information from medical records before sharing the data with other researchers. Different computer-assisted methods are evaluated for removing and replacing protected health information (PHI) from free-text nursing notes collected in the hospital intensive care unit. A semi-automated method was developed to allow clinicians to highlight PHI on the screen of a tablet PC and to compare and combine the selections of different experts reading the same notes. Expert adjudication demonstrated that inter-human variability was high, with few false positives and many false negatives. A preliminary automated de-identification algorithm generated few false negatives but many false positives. A second automated algorithm was developed using the successful portions of the first algorithm and incorporating other heuristic methods to improve overall performance. A large de-identified collection of nursing notes was re-identified with realistic surrogate (but unprotected) dates, serial numbers, names, and phrases to form a "gold standard" reference database of over 2600 notes (approximately 340,000 words) with over 1800 labeled instances of PHI. This gold standard database of nursing notes and the Java source code used to evaluate algorithm performance will be made freely available on the Physionet web site in order to facilitate the development and validation of future de-identification algorithms. by Margaret Douglass. M.Eng. 2006-07-13T15:13:32Z 2006-07-13T15:13:32Z 2005 2005 Thesis http://hdl.handle.net/1721.1/33299 62279367 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 70 leaves 3923649 bytes 3926254 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Douglass, Margaret, 1981-
Computer-assisted de-identification of free-text nursing notes
title Computer-assisted de-identification of free-text nursing notes
title_full Computer-assisted de-identification of free-text nursing notes
title_fullStr Computer-assisted de-identification of free-text nursing notes
title_full_unstemmed Computer-assisted de-identification of free-text nursing notes
title_short Computer-assisted de-identification of free-text nursing notes
title_sort computer assisted de identification of free text nursing notes
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/33299
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