2026-09-18T21:22:21Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/416222022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Roger G. Mark.
Neamatullah, Ishna
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T16:02:04Z
2008-05-19T16:02:04Z
2006
2006
http://hdl.handle.net/1721.1/41622
216883891
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 62-64).
This paper presents a de-identification study at the Harvard-MIT Division of Health Science and Technology (HST) to automatically de-identify confidential patient information from text medical records used in intensive care units (ICUs). Patient records are a vital resource in medical research. Before such records can be made available for research studies, protected health information (PHI) must be thoroughly scrubbed according to HIPAA specifications to preserve patient confidentiality. Manual de-identification on large databases tends to be prohibitively expensive, time-consuming and prone to error, making a computerized algorithm an urgent need for large-scale de-identification purposes. We have developed an automated pattern-matching deidentification algorithm that uses medical and hospital-specific information. The current version of the algorithm has an overall sensitivity of around 0.87 and an approximate positive predictive value of 0.63. In terms of sensitivity, it performs significantly better than 1 person (0.81) but not quite as well as a consensus of 2 human de-identifiers (0.94). The algorithm will be published as open-source software, and the de-identified medical records will be incorporated into HST's Multi-Parameter Intelligent Monitoring for Intensive Care (MIMIC II) physiologic database.
by Ishna Neamatullah.
M.Eng.
73 p.
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Automated de-identification of free-text medical records
Thesis
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<Abstract>This paper presents a de-identification study at the Harvard-MIT Division of Health Science and Technology (HST) to automatically de-identify confidential patient information from text medical records used in intensive care units (ICUs). Patient records are a vital resource in medical research. Before such records can be made available for research studies, protected health information (PHI) must be thoroughly scrubbed according to HIPAA specifications to preserve patient confidentiality. Manual de-identification on large databases tends to be prohibitively expensive, time-consuming and prone to error, making a computerized algorithm an urgent need for large-scale de-identification purposes. We have developed an automated pattern-matching deidentification algorithm that uses medical and hospital-specific information. The current version of the algorithm has an overall sensitivity of around 0.87 and an approximate positive predictive value of 0.63. In terms of sensitivity, it performs significantly better than 1 person (0.81) but not quite as well as a consensus of 2 human de-identifiers (0.94). The algorithm will be published as open-source software, and the de-identified medical records will be incorporated into HST's Multi-Parameter Intelligent Monitoring for Intensive Care (MIMIC II) physiologic database.</Abstract>
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