Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 25 entries
Sorted by: Best Match Show Resources per page
A unified framework for evaluating the risk of re-identification of text de-identification tools.

Journal of biomedical informatics

Scaiano M, Middleton G, Arbuckle L, Kolhatkar V, Peyton L, Dowling M, Gipson DS, El Emam K.
PMID: 27426236
J Biomed Inform. 2016 Oct;63:174-183. doi: 10.1016/j.jbi.2016.07.015. Epub 2016 Jul 15.

OBJECTIVES: It has become regular practice to de-identify unstructured medical text for use in research using automatic methods, the goal of which is to remove patient identifying information to minimize re-identification risk. The metrics commonly used to determine if...

Cite
Scaiano M, Middleton G, Arbuckle L, et al. A unified framework for evaluating the risk of re-identification of text de-identification tools. J Biomed Inform. 2016;63:174-183doi: 10.1016/j.jbi.2016.07.015.
Scaiano, M., Middleton, G., Arbuckle, L., Kolhatkar, V., Peyton, L., Dowling, M., Gipson, D. S., & El Emam, K. (2016). A unified framework for evaluating the risk of re-identification of text de-identification tools. Journal of biomedical informatics, 63174-183. https://doi.org/10.1016/j.jbi.2016.07.015
Scaiano, Martin, et al. "A unified framework for evaluating the risk of re-identification of text de-identification tools." Journal of biomedical informatics vol. 63 (2016): 174-183. doi: https://doi.org/10.1016/j.jbi.2016.07.015
Scaiano M, Middleton G, Arbuckle L, Kolhatkar V, Peyton L, Dowling M, Gipson DS, El Emam K. A unified framework for evaluating the risk of re-identification of text de-identification tools. J Biomed Inform. 2016 Oct;63:174-183. doi: 10.1016/j.jbi.2016.07.015. Epub 2016 Jul 15. PMID: 27426236.
Copy Download .nbib Format: NLM
Findings from 2017 on Health Information Management.

Yearbook of medical informatics

Bloomrosen M, Berner ES.
PMID: 30157507
Yearb Med Inform. 2018 Aug;27(1):67-73. doi: 10.1055/s-0038-1667072. Epub 2018 Aug 29.

OBJECTIVE: To summarize the recent literature and research and present a selection of the best papers published in 2017 in the field of Health Information Management (HIM) and Health Informatics.METHODS: A systematic review of the literature was performed by...

Cite
Bloomrosen M, Berner ES. Findings from 2017 on Health Information Management. Yearb Med Inform. 2018;27(1):67-73doi: 10.1055/s-0038-1667072.
Bloomrosen, M., Berner, E. S. (2018). Findings from 2017 on Health Information Management. Yearbook of medical informatics, 27(1), 67-73. https://doi.org/10.1055/s-0038-1667072
Bloomrosen, Meryl, et al. "Findings from 2017 on Health Information Management." Yearbook of medical informatics vol. 27,1 (2018): 67-73. doi: https://doi.org/10.1055/s-0038-1667072
Bloomrosen M, Berner ES. Findings from 2017 on Health Information Management. Yearb Med Inform. 2018 Aug;27(1):67-73. doi: 10.1055/s-0038-1667072. Epub 2018 Aug 29. PMID: 30157507; PMCID: PMC6115240.
Copy Download .nbib Format: NLM
The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge.

Journal of biomedical informatics

Bui DDA, Wyatt M, Cimino JJ.
PMID: 28478268
J Biomed Inform. 2017 Nov;75:S54-S61. doi: 10.1016/j.jbi.2017.05.001. Epub 2017 May 03.

Clinical narratives (the text notes found in patients' medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use. Manual de-identification is considered to be...

Cite
Bui DDA, Wyatt M, Cimino JJ. The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge. J Biomed Inform. 2017;75:S54-S61doi: 10.1016/j.jbi.2017.05.001.
Bui, D. D. A., Wyatt, M., & Cimino, J. J. (2017). The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge. Journal of biomedical informatics, 75S54-S61. https://doi.org/10.1016/j.jbi.2017.05.001
Bui, Duy Duc An, et al. "The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge." Journal of biomedical informatics vol. 75 (2017): S54-S61. doi: https://doi.org/10.1016/j.jbi.2017.05.001
Bui DDA, Wyatt M, Cimino JJ. The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge. J Biomed Inform. 2017 Nov;75:S54-S61. doi: 10.1016/j.jbi.2017.05.001. Epub 2017 May 03. PMID: 28478268; PMCID: PMC5670015.
Copy Download .nbib Format: NLM
Customization scenarios for de-identification of clinical notes.

BMC medical informatics and decision making

Hartman T, Howell MD, Dean J, Hoory S, Slyper R, Laish I, Gilon O, Vainstein D, Corrado G, Chou K, Po MJ, Williams J, Ellis S, Bee G, Hassidim A, Amira R, Beryozkin G, Szpektor I, Matias Y.
PMID: 32000770
BMC Med Inform Decis Mak. 2020 Jan 30;20(1):14. doi: 10.1186/s12911-020-1026-2.

BACKGROUND: Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about...

Cite
Hartman T, Howell MD, Dean J, et al. Customization scenarios for de-identification of clinical notes. BMC Med Inform Decis Mak. 2020;20(1):14doi: 10.1186/s12911-020-1026-2.
Hartman, T., Howell, M. D., Dean, J., Hoory, S., Slyper, R., Laish, I., Gilon, O., Vainstein, D., Corrado, G., Chou, K., Po, M. J., Williams, J., Ellis, S., Bee, G., Hassidim, A., Amira, R., Beryozkin, G., Szpektor, I., & Matias, Y. (2020). Customization scenarios for de-identification of clinical notes. BMC medical informatics and decision making, 20(1), 14. https://doi.org/10.1186/s12911-020-1026-2
Hartman, Tzvika, et al. "Customization scenarios for de-identification of clinical notes." BMC medical informatics and decision making vol. 20,1 (2020): 14. doi: https://doi.org/10.1186/s12911-020-1026-2
Hartman T, Howell MD, Dean J, Hoory S, Slyper R, Laish I, Gilon O, Vainstein D, Corrado G, Chou K, Po MJ, Williams J, Ellis S, Bee G, Hassidim A, Amira R, Beryozkin G, Szpektor I, Matias Y. Customization scenarios for de-identification of clinical notes. BMC Med Inform Decis Mak. 2020 Jan 30;20(1):14. doi: 10.1186/s12911-020-1026-2. PMID: 32000770; PMCID: PMC6993314.
Copy Download .nbib Format: NLM
Who Will Drive the Change? Democratizing Health Data.

American journal of public health

Boulware LE, Lyn M.
PMID: 30865498
Am J Public Health. 2019 Apr;109(4):547-548. doi: 10.2105/AJPH.2019.304985.

No abstract available.

Cite
Boulware LE, Lyn M. Who Will Drive the Change? Democratizing Health Data. Am J Public Health. 2019;109(4):547-548doi: 10.2105/AJPH.2019.304985.
Boulware, L. E., & Lyn, M. (2019). Who Will Drive the Change? Democratizing Health Data. American journal of public health, 109(4), 547-548. https://doi.org/10.2105/AJPH.2019.304985
Boulware, L Ebony, and Lyn, Michelle. "Who Will Drive the Change? Democratizing Health Data." American journal of public health vol. 109,4 (2019): 547-548. doi: https://doi.org/10.2105/AJPH.2019.304985
Boulware LE, Lyn M. Who Will Drive the Change? Democratizing Health Data. Am J Public Health. 2019 Apr;109(4):547-548. doi: 10.2105/AJPH.2019.304985. PMID: 30865498; PMCID: PMC6417594.
Copy Download .nbib Format: NLM
Data sharing platforms for de-identified data from human clinical trials.

Clinical trials (London, England)

Huser V, Shmueli-Blumberg D.
PMID: 29676586
Clin Trials. 2018 Aug;15(4):413-423. doi: 10.1177/1740774518769655. Epub 2018 Apr 20.

Data sharing of de-identified individual participant data is being adopted by an increasing number of sponsors of human clinical trials. In addition to standardizing data syntax for shared trial data, semantic integration of various data elements is the focus...

Cite
Huser V, Shmueli-Blumberg D. Data sharing platforms for de-identified data from human clinical trials. Clin Trials. 2018;15(4):413-423doi: 10.1177/1740774518769655.
Huser, V., & Shmueli-Blumberg, D. (2018). Data sharing platforms for de-identified data from human clinical trials. Clinical trials (London, England), 15(4), 413-423. https://doi.org/10.1177/1740774518769655
Huser, Vojtech, and Shmueli-Blumberg, Dikla. "Data sharing platforms for de-identified data from human clinical trials." Clinical trials (London, England) vol. 15,4 (2018): 413-423. doi: https://doi.org/10.1177/1740774518769655
Huser V, Shmueli-Blumberg D. Data sharing platforms for de-identified data from human clinical trials. Clin Trials. 2018 Aug;15(4):413-423. doi: 10.1177/1740774518769655. Epub 2018 Apr 20. PMID: 29676586.
Copy Download .nbib Format: NLM
De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.

Journal of biomedical informatics

Stubbs A, Filannino M, Uzuner Ö.
PMID: 28614702
J Biomed Inform. 2017 Nov;75:S4-S18. doi: 10.1016/j.jbi.2017.06.011. Epub 2017 Jun 11.

The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks. Track 1 focused on de-identification of a new corpus of 1000 psychiatric intake records. This track tackled de-identification in two sub-tracks: Track 1.A was a "sight unseen"...

Cite
Stubbs A, Filannino M, Uzuner Ö. De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1. J Biomed Inform. 2017;75:S4-S18doi: 10.1016/j.jbi.2017.06.011.
Stubbs, A., Filannino, M., & Uzuner, �. �. (2017). De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1. Journal of biomedical informatics, 75S4-S18. https://doi.org/10.1016/j.jbi.2017.06.011
Stubbs, Amber, et al. "De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1." Journal of biomedical informatics vol. 75 (2017): S4-S18. doi: https://doi.org/10.1016/j.jbi.2017.06.011
Stubbs A, Filannino M, Uzuner Ö. De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1. J Biomed Inform. 2017 Nov;75:S4-S18. doi: 10.1016/j.jbi.2017.06.011. Epub 2017 Jun 11. PMID: 28614702; PMCID: PMC5705537.
Copy Download .nbib Format: NLM
Preserving temporal relations in clinical data while maintaining privacy.

Journal of the American Medical Informatics Association : JAMIA

Hripcsak G, Mirhaji P, Low AF, Malin BA.
PMID: 27013522
J Am Med Inform Assoc. 2016 Nov;23(6):1040-1045. doi: 10.1093/jamia/ocw001. Epub 2016 Mar 24.

OBJECTIVE: Maintaining patient privacy is a challenge in large-scale observational research. To assist in reducing the risk of identifying study subjects through publicly available data, we introduce a method for obscuring date information for clinical events and patient characteristics.METHODS:...

Cite
Hripcsak G, Mirhaji P, Low AF, et al. Preserving temporal relations in clinical data while maintaining privacy. J Am Med Inform Assoc. 2016;23(6):1040-1045doi: 10.1093/jamia/ocw001.
Hripcsak, G., Mirhaji, P., Low, A. F., & Malin, B. A. (2016). Preserving temporal relations in clinical data while maintaining privacy. Journal of the American Medical Informatics Association : JAMIA, 23(6), 1040-1045. https://doi.org/10.1093/jamia/ocw001
Hripcsak, George, et al. "Preserving temporal relations in clinical data while maintaining privacy." Journal of the American Medical Informatics Association : JAMIA vol. 23,6 (2016): 1040-1045. doi: https://doi.org/10.1093/jamia/ocw001
Hripcsak G, Mirhaji P, Low AF, Malin BA. Preserving temporal relations in clinical data while maintaining privacy. J Am Med Inform Assoc. 2016 Nov;23(6):1040-1045. doi: 10.1093/jamia/ocw001. Epub 2016 Mar 24. PMID: 27013522; PMCID: PMC5070517.
Copy Download .nbib Format: NLM
Anonymisation of geographical distance matrices via Lipschitz embedding.

International journal of health geographics

Kroll M, Schnell R.
PMID: 26739310
Int J Health Geogr. 2016 Jan 07;15:1. doi: 10.1186/s12942-015-0031-7.

BACKGROUND: Anonymisation of spatially referenced data has received increasing attention in recent years. Whereas the research focus has been on the anonymisation of point locations, the disclosure risk arising from the publishing of inter-point distances and corresponding anonymisation methods...

Cite
Kroll M, Schnell R. Anonymisation of geographical distance matrices via Lipschitz embedding. Int J Health Geogr. 2016;15:1doi: 10.1186/s12942-015-0031-7.
Kroll, M., & Schnell, R. (2016). Anonymisation of geographical distance matrices via Lipschitz embedding. International journal of health geographics, 151. https://doi.org/10.1186/s12942-015-0031-7
Kroll, Martin, and Schnell, Rainer. "Anonymisation of geographical distance matrices via Lipschitz embedding." International journal of health geographics vol. 15 (2016): 1. doi: https://doi.org/10.1186/s12942-015-0031-7
Kroll M, Schnell R. Anonymisation of geographical distance matrices via Lipschitz embedding. Int J Health Geogr. 2016 Jan 07;15:1. doi: 10.1186/s12942-015-0031-7. PMID: 26739310; PMCID: PMC4704375.
Copy Download .nbib Format: NLM
Deidentification of facial photographs: a survey of editorial policies and practices.

Journal of medical ethics

Roguljić M, Buljan I, Veček N, Dragun R, Marušić M, Wager E, Marušić A.
PMID: 32253363
J Med Ethics. 2022 Jan;48(1):56-60. doi: 10.1136/medethics-2019-105823. Epub 2020 Apr 06.

We analysed all journals from two Journal Citation Reports (JCR) categories: 'Dentistry, Oral Surgery and Medicine' and 'Otorhinolaryngology' published in 2018 for their policies on publishing facial photographs and actual practices of publishing these photographs in articles. We extracted...

Cite
Roguljić M, Buljan I, Veček N, et al. Deidentification of facial photographs: a survey of editorial policies and practices. J Med Ethics. 2020;48(1):56-60doi: 10.1136/medethics-2019-105823.
Roguljić, M., Buljan, I., Veček, N., Dragun, R., Marušić, M., Wager, E., & Marušić, A. (2022). Deidentification of facial photographs: a survey of editorial policies and practices. Journal of medical ethics, 48(1), 56-60. https://doi.org/10.1136/medethics-2019-105823
Roguljić, Marija, et al. "Deidentification of facial photographs: a survey of editorial policies and practices." Journal of medical ethics vol. 48,1 (2022): 56-60. doi: https://doi.org/10.1136/medethics-2019-105823
Roguljić M, Buljan I, Veček N, Dragun R, Marušić M, Wager E, Marušić A. Deidentification of facial photographs: a survey of editorial policies and practices. J Med Ethics. 2022 Jan;48(1):56-60. doi: 10.1136/medethics-2019-105823. Epub 2020 Apr 06. PMID: 32253363.
Copy Download .nbib Format: NLM
Clinical records anonymisation and text extraction (CRATE): an open-source software system.

BMC medical informatics and decision making

Cardinal RN.
PMID: 28441940
BMC Med Inform Decis Mak. 2017 Apr 26;17(1):50. doi: 10.1186/s12911-017-0437-1.

BACKGROUND: Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information. Patient-identifiable information cannot in general be shared outside clinical care teams without explicit consent, but anonymisation/de-identification allows research uses of...

Cite
Cardinal RN. Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC Med Inform Decis Mak. 2017;17(1):50doi: 10.1186/s12911-017-0437-1.
Cardinal, R. N. (2017). Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC medical informatics and decision making, 17(1), 50. https://doi.org/10.1186/s12911-017-0437-1
Cardinal, Rudolf N. "Clinical records anonymisation and text extraction (CRATE): an open-source software system." BMC medical informatics and decision making vol. 17,1 (2017): 50. doi: https://doi.org/10.1186/s12911-017-0437-1
Cardinal RN. Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC Med Inform Decis Mak. 2017 Apr 26;17(1):50. doi: 10.1186/s12911-017-0437-1. PMID: 28441940; PMCID: PMC5405523.
Copy Download .nbib Format: NLM
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

Journal of the American Medical Informatics Association : JAMIA

Xiao C, Choi E, Sun J.
PMID: 29893864
J Am Med Inform Assoc. 2018 Oct 01;25(10):1419-1428. doi: 10.1093/jamia/ocy068.

OBJECTIVE: To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open...

Cite
Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc. 2018;25(10):1419-1428doi: 10.1093/jamia/ocy068.
Xiao, C., Choi, E., & Sun, J. (2018). Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association : JAMIA, 25(10), 1419-1428. https://doi.org/10.1093/jamia/ocy068
Xiao, Cao, et al. "Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review." Journal of the American Medical Informatics Association : JAMIA vol. 25,10 (2018): 1419-1428. doi: https://doi.org/10.1093/jamia/ocy068
Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc. 2018 Oct 01;25(10):1419-1428. doi: 10.1093/jamia/ocy068. PMID: 29893864; PMCID: PMC6188527.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 25 entries