Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)

By Shawn N. Murphy, Griffin M. Weber, M. Mendis, Vivian S. Gainer, H. Chueh, Susanne E. Churchill, Isaac S. Kohane. 2010

976 citations recorded by the source corpus.

Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases ("data marts") can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software.

Subfields: Health Information Management, Information Systems and Management, Molecular Biology

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Citations in the corpus

Federated Tensor Factorization for Computational Phenotyping (2017, methodology): “…min-imumcountthreshold(e.g., 10) foraggregate datasharing [25]. same threshold is used in Informatics for Integrating Biology & the Bedsides (I2B2) [24], a famous system developed by National Center for Biomedical Computing based at Partners HealthCare. real-world challenges motivate the…”
L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018, methodology): “For example, PCORnet data privacy guidance does not allow record-level research participant information sharing and it recommends aminimumcount threshold (e.g., 10) for aggregate data sharing [25]. e same threshold is used in Informatics for Integrating Biology & the Bedsides (I2B2) [24], a famous s”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “NIH-funded National Center for Biomedical Computing with a primary mission to provide researchers with informatics tools to leverage EHRs for clinical and genetic research [72].”
The Human Phenotype Ontology in 2021 (2020, methodology): “One solution is to generate mappings to common data models (CDM) as well as tools that integrate different EHR data, such as Informatics for Integrating Biology and the Bedside (i2b2) (64) and Observational Health Data Sciences and Informatics’s Observational Medical Outcomes Partnership (OMOP) (65)”
Machine Learning and Decision Support in Critical Care (2016, methodology): “The Integrating Biology and the Bedside (i2b2) project is a successful application of both methods: data is stored locally at each institution with PHI, and researchers can query for aggregate summaries of the data without access to individual level information [17].”
Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (2013, background): “Examples include large health care delivery organizations with extensive EHR capabilities, such as Kaiser Permanente and the Veterans Health Administration, and major initiatives in data sharing between different organizations, such as the Nationwide Health Information Network, the Care Connectivity”
Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE) (2015, positive): “Such lists may be prepared ad hoc (e.g., of patients currently staying in an inpatient unit) or systematically identified through claims data, disease-specific patient registries, or research informatics tools such as the i2b2 Workbench [46].”
Machine Learning Methods to Predict Diabetes Complications (2018, methodology): “These data were enriched with the administrative data available in local health care agency, and stored into a specialized data warehouse called the i2b2 MOSAIC Data Warehouse.(10) As clinical data were available only after the first visit at the hospital, patients who had already developed complica”
Large language models are few-shot clinical information extractors (2022, methodology): “benchmarking few-shot clinical information extraction methods, as many shared clinical corpora (Murphy et al., 2010; Henry et al., 2020; Johnson et al., 2016) have data use agreements that prevent their use with LLM APIs such as OpenAI’s.”
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence (2019, methodology): “in a federated manner. Our method enables efficient utilization of data from all participating silos in a federated data network. Examples of such federated medical data networks include i2b2 network(Murphy et al. 2010), Undiagnosed Diseases Network (UDN)(Gahl, Wise, and Ashley 2015) and European Re”