iDASH: integrating data for analysis, anonymization, and sharing

Ohno-Machado, Lucila; Bafna, Vineet; Boxwala, Aziz A.; Chapman, Brian E.; Chapman, Wendy W.; Chaudhuri, Kamalika; Day, Michele E.; Farcas, Claudiu; Heintzman, Nathaniel D.; Jiang, Xiaoqian; Kim, Hyeoneui; Kim, Jihoon; Matheny, Michael E.; Resnic, Frederic S.; Vinterbo, Staal A.

doi:10.1136/amiajnl-2011-000538

Cited by 127 publications

(75 citation statements)

References 33 publications

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“…There have been several shared tasks that bring researchers in clinical NLP together to solve, evaluate and compare different methods. Additionally, there are shared computing resources that aggregate several NLP tools to facilitate the work of researchers, such as the NLP environment in iDASH [50]. The Online Registry of Biomedical Informatics Tools (ORBIT) 24 project is another platform for sharing and collaborating for biomedical researchers in order to create and maintain a software registry, in addition to knowledge bases and data sets.…”

Section: Discussionmentioning

confidence: 99%

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Doan

Conway

Phương³

et al. 2014

Methods in Molecular Biology

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In modern electronic medical records (EMR) much of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge. IntroductionIn modern electronic medical records (EMR) most of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -is not provided in structured data fields, but are rather encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source, converting unstructured text to structured, actionable data for use in applications for clinical decision support, quality assurance, and public health surveillance. There are currently many NLP systems that have been 2 successfully applied to biomedical text. It is not our goal to review all of them in this chapter, but rather to provide an overview of how the field evolved from producing monolithic software built on platforms that were available at the time they were developed to contemporary component-based systems built on top of general frameworks. More importantly, the performance of these systems is tightly associated with their "ingredients" (i.e., modules that are used to form its background knowledge), and how these modules are combined on top of the general framework. We highlight certain systems based on their landmark status as well as on the diversity of components and frameworks they are based on. [7]. The review in this chapter differs from previous work in that it emphasizes the historical development of landmark clinical NLP systems, and presents each system in light of a unified system architecture.We consider that each NLP system in biomedicine contains two main components: biomedical background knowledge and a framework that integrates NLP tools. In the rest of this paper, we will first outline our model architecture for NLP systems in biomedicine, before going on to review and summarize representative NLP systems, starting with an early NLP system, LSP-MLP, and closing our discussion with the presentation of a more recent system, cTAKES. Finally, we will discuss...

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Section: Discussionmentioning

confidence: 99%

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Doan

Conway

Phương³

et al. 2014

Methods in Molecular Biology

Self Cite

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“…NHS Digital provides access to national data from the UK National Health Service and provides a registry of safe havens 22 through which researchers can connect to and analyse these data. Examples from other countries include the Australian SURE project 23 and the NIH funded iDASH project that has created tools for secure data access, data analysis and privacy-preserving data sharing (Ohno-Machado et al 2012).…”

Section: Alternative Approachesmentioning

confidence: 99%

DataSHIELD – New Directions and Dimensions

Wilson

Butters

Avraam

et al. 2017

Data Science Journal

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In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data.Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences.

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“…Dif erent models for data sharing in a research community cloud are currently being investigated-for example, in the Johns Hopkins Institute for NanoBioTechnology (http://releases.jhu.edu/2012/11/06/ collecting-cancer-data-in-the-cloud/) and in the iDASH National Center for Biomedical Computing (12). iDASH stands for "integrating data for analysis, anonymization, and sharing" and is one of six centers funded Fig.…”

Section: State Of Technologymentioning

confidence: 99%