The MITRE Identification Scrubber Toolkit: design, training, and assessment.
about
Ease of adoption of clinical natural language processing software: An evaluation of five systems.Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1Optimizing annotation resources for natural language de-identification via a game theoretic frameworkCombining knowledge- and data-driven methods for de-identification of clinical narrativesDevelopment and evaluation of a de-identification procedure for a case register sourced from mental health electronic recordsBoB, a best-of-breed automated text de-identification system for VHA clinical documentsGeneralizability and comparison of automatic clinical text de-identification methods and resourcesPreparing an annotated gold standard corpus to share with extramural investigators for de-identification researchGenetic data sharing and privacy.A system for de-identifying medical message board text.A context-blocks model for identifying clinical relationships in patient records.Assisted annotation of medical free text using RapTAT.Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals.Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.De-identification of clinical narratives through writing complexity measures.Chapter 13: Mining electronic health records in the genomics era.Improved de-identification of physician notes through integrative modeling of both public and private medical text.Voice-dictated versus typed-in clinician notes: linguistic properties and the potential implications on natural language processingP2P watch: personal health information detection in peer-to-peer file-sharing networksLearning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.Evaluation of PHI Hunter in Natural Language Processing Research.Hedging their mets: the use of uncertainty terms in clinical documents and its potential implications when sharing the documents with patientsBuilding gold standard corpora for medical natural language processing tasks.Large-scale evaluation of automated clinical note de-identification and its impact on information extraction.Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice.Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text.Assessing the readability of ClinicalTrials.gov.Electronic medical records as a tool in clinical pharmacology: opportunities and challenges.Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortiumASLForm: an adaptive self learning medical form generating system.Location bias of identifiers in clinical narratives.Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification.Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical textPreserving medical correctness, readability and consistency in de-identified health records.Automatic detection of protected health information from clinic narratives.A De-identification method for bilingual clinical texts of various note types.Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports.A hybrid approach to automatic de-identification of psychiatric notes.Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.Scalable Iterative Classification for Sanitizing Large-Scale Datasets.
P2860
Q27310230-77F3ADCD-042C-43C6-B5A7-EBED1FAEB891Q28085066-6439A91E-6859-43CB-8565-F470194699E6Q28597276-9FD56FD7-1082-48C5-B937-4AEBD644C36AQ28598184-40AFFF09-43FA-4B70-9DB9-921F1558DC63Q28681582-AEFCF7F7-2824-4E59-9DCA-9D8CC9E495FDQ28709508-34A10C97-D79C-49B4-AB8A-FDDFB79DDC29Q28710039-41906943-0032-4A13-868F-52AA4151874CQ29030634-CEAC834B-502D-4F3C-A2A7-1F94F2FD6A4AQ30862406-FE1A82FE-FFFE-458F-9054-3349906FF05CQ33928254-A72D9E84-BF1D-4A04-B768-015E1DE323F3Q33928259-042E05B0-0AD3-450E-B0D9-993F3F9F99C2Q34101907-21AD6F25-8D02-4B03-9482-4D018289E1E8Q34101921-5C09E18F-E60F-4C7C-9CF5-F5FC48A2BD52Q34354291-99D63D2E-5C98-4BDF-8F75-80C426EB4230Q34434532-F90B2DE6-74AB-48BC-B1D4-448127491AF8Q34539676-021554FA-F519-4650-8951-11F5C716D0A4Q35003628-585836D6-05C9-4390-AE7F-8B69DF3E402AQ35625379-43643819-C981-456C-B14E-90E2DD903EFBQ36152166-9C3FE69E-9625-42D9-85F8-0170793C900EQ36397886-6B1F55FB-C504-42AB-99A0-CFF06BA589BFQ36429186-7BECF46A-E612-4391-834E-925DE2364744Q36519073-36805B05-28EF-4C79-A35D-9BA149474D9FQ36519175-41B47DC6-D6EC-4BDF-9B97-4D2D6F5CA293Q36563315-C43B3E1E-2B70-4C47-97B7-8ED9BED77576Q36702588-C3AAD970-87F0-436E-A10B-D7FDCF170700Q36800587-BEDC17A2-4937-4F80-A9F4-CD0C2D41E2DAQ37229244-93BA183A-1E56-4471-863E-ECE0EC3E90CFQ37286031-AFEFCB18-C24B-4DED-B076-DFE7A4624854Q37389462-020BE017-690A-4E64-B0F3-4ECEE94D8742Q37508822-4B50B50C-2CE0-49B6-9EC0-5B2A10BDC6B4Q37508945-A2F3D767-16FF-4176-8857-0323BBE980A6Q37541267-E5F5655F-E125-41C3-A135-54549A03C8FAQ38214496-23CAA76E-3838-48B2-9B34-2CAC3B935115Q38392354-74879A3A-1ADC-4897-85C1-2E0F076E21A5Q38406923-E46C651C-1AF7-4A27-BD48-84FAC2F4716BQ38418332-5E52FA7E-5EE9-404A-B5C4-5C2E1E5FF0D1Q38598158-256F2C40-764B-4AAD-9FD6-E1E536F5338FQ38731329-FE4966C1-BCED-4FB0-BF6F-708CE2B61867Q38832328-9528948A-0799-4652-9591-7A813B0E5AC7Q42692730-37078C74-320D-499D-AD3C-F9F6F90E7F7A
P2860
The MITRE Identification Scrubber Toolkit: design, training, and assessment.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
@zh
2010年论文
@zh-cn
name
The MITRE Identification Scrubber Toolkit: design, training, and assessment.
@en
type
label
The MITRE Identification Scrubber Toolkit: design, training, and assessment.
@en
prefLabel
The MITRE Identification Scrubber Toolkit: design, training, and assessment.
@en
P2093
P1476
The MITRE Identification Scrubber Toolkit: design, training, and assessment.
@en
P2093
Ben Wellner
Cheryl Clark
David Hanauer
John Aberdeen
Lynette Hirschman
Reyyan Yeniterzi
Samuel Bayer
P304
P356
10.1016/J.IJMEDINF.2010.09.007
P577
2010-10-14T00:00:00Z