The MITRE Identification Scrubber Toolkit: Design, training, and assessment

Aberdeen, John S.; Bayer, Samuel; Yeniterzi, Reyyan; Wellner, Benjamin; Clark, Cheryl; Hanauer, David A.; Malin, Bradley; Hirschman, Lynette

doi:10.1016/j.ijmedinf.2010.09.007

Cited by 110 publications

(110 citation statements)

References 9 publications

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“…PMS_DN uses a combination of two independent anonymizers—the MITRE MIST anonymizer (Aberdeen et al, 2010) and the Scrubber toolkit (McMurry et al, 2013) in the Apache cTAKES NLP engine—to remove PHI elements from the clinical notes. In a study (McMurry et al, 2013), the Scrubber toolkit in Apache cTAKES identified and removed approximately 98% of the PHI elements (Recall = 98%) from a test corpus of clinical notes selected from the i2b2 De‐Identification Challenge dataset (Uzuner, Luo, & Szolovits, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Then, the MITRE MIST tool (Aberdeen et al, 2010) and the Scrubber toolkit (McMurry, Fitch, Savova, Kohane, & Reis, 2013) in the Apache cTAKES NLP engine were used to erase Protected Health Information (PHI) elements from the text. Following de‐identification, the Apache cTAKES NLP engine (Savova et al, 2010) was deployed to extract knowledge by identifying occurrences of concepts defined in the Unified Medical Language System (UMLS) (Bodenreider, 2004) in the text.…”

Section: Methodsmentioning

confidence: 99%

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Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

Kothari

Wack

Hassen‐Khodja

et al. 2017

American J of Med Genetics Pt B

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The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan‐McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan‐McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype–genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes—two heterogeneous, underutilized sources of knowledge about patient phenotypes—with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portal https://pmsdn.hms.harvard.edu. PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high‐quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

Kothari

Wack

Hassen‐Khodja

et al. 2017

American J of Med Genetics Pt B

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“…Some are more generalizable than others, and certain methods perform better with some types of PHI than others [71,72]. Recent examples such as MIST [73], BoB [74], Anonym [75], and several systems developed for the i2b2 NLP challenges [76,77], allow for good accuracy and very limited impact on clinical information. [78] Replacing PHI with realistic surrogates [79] and adding biomedical scientific literature text [80] allowed for improved performance.…”

Section: Impact On Reusementioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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“…For example, Stanford NER (Finkel et al, 2005), ABNER (Settles, 2005), the MITRE Identification Scrubber Toolkit (MIST) (Aberdeen et al, 2010), (Boag et al, 2015), BANNER (Leaman et al, 2008) and NERsuite (Cho et al, 2010) rely on CRFs. GAPSCORE uses SVMs (Chang et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Specia

Post

Paul

2017

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The MITRE Identification Scrubber Toolkit: Design, training, and assessment

Cited by 110 publications

References 9 publications

Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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