Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform

Wagholikar, Kavishwar B.; Mendis, Michael; Dessai, Pralav; Martínez-Sanz, Javier; Law, Sindy; Gilson, Micheal; Sanders, Stephan J.; Vangala, Mahesh; Bell, Douglas S.; Murphy, Shawn N.

doi:10.1177/1178222618777749

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…Tools for this purpose include i2b2 [99], SHRINE [100], HiGHmed [101], tranSMART [102], etc. ; many of which are being adopted in healthcare settings through the world [63–65]. Publicly available platforms such as mi2b2 [66] and ChRIS [103, 104] permit the download of MRI data from Radiology archives with patient MRNs obtained from registry or hospital-wide searches [66].…”

Section: Discussionmentioning

confidence: 99%

“…A second source is the hospital-wide database. In the big data era, an increasing number of hospitals around the world have informatics tools that allow authorized personnel to search patients by diagnosis, or by the International Classification of Diseases (ICD) codes [63–65]. From our registry data, we identified a list of ICD codes commonly used for HIE (Table 1).…”

Section: Methods and Designmentioning

confidence: 99%

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Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

et al. 2019

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BackgroundSecondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magnetic Resonance Images (MRI) and clinical records of neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed for MRI-based HIE lesion detection and outcome prediction.MethodsThis retrospective study will use clinical registries and big data informatics tools to build a multi-site dataset that contains structural and diffusion MRI, clinical information including hospital course, short-term outcomes (during infancy), and long-term outcomes (~ 2 years of age) for at least 300 patients from multiple hospitals.DiscussionWithin machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts.Trial Registration Not applicable. This study protocol mines existing clinical data thus does not meet the ICMJE definition of a clinical trial that requires registration

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Section: Discussionmentioning

confidence: 99%

Section: Methods and Designmentioning

confidence: 99%

Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

et al. 2019

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“…Furthermore, when deploying such systems in production environments, additional aspects such as transport encryption and password management need to be considered. These and further functionalities are not supported by existing cloud-based deployment solutions for i2b2 and tranSMART, such as the Integrated Data Repository Toolkit (IDRT) [ 26 ], i2b2 Quickstart [ 27 ], or the prebuilt images available on Docker Hub [ 28 ] (see the Discussion section for an in-depth comparison).…”

Section: Methodsmentioning

confidence: 99%

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Spengler¹,

Lang²,

Mahapatra³

et al. 2020

JMIR Med Inform

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Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

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“…We obtained data for 193,808 patients with a coded diagnosis of heart failure from the institutional research patient data repository [12-15]. The eligibility criteria for the study included adult patients that had a diagnosis of HFrEF, and whose cardiac care was managed primarily by a cardiologist at BWH.…”

Section: Methodsmentioning

confidence: 99%

Phenotyping to Facilitate Accrual for a Cardiovascular Intervention

et al. 2019

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Background The conventional approach for clinical studies is to identify a cohort of potentially eligible patients and then screen for enrollment. In an effort to reduce the cost and manual effort involved in the screening process, several studies have leveraged electronic health records (EHR) to refine cohorts to better match the eligibility criteria, which is referred to as phenotyping. We extend this approach to dynamically identify a cohort by repeating phenotyping in alternation with manual screening. Methods Our approach consists of multiple screen cycles. At the start of each cycle, the phenotyping algorithm is used to identify eligible patients from the EHR, creating an ordered list such that patients that are most likely eligible are listed first. This list is then manually screened, and the results are analyzed to improve the phenotyping for the next cycle. We describe the preliminary results and challenges in the implementation of this approach for an intervention study on heart failure. Results A total of 1,022 patients were screened, with 223 (23%) of patients being found eligible for enrollment into the intervention study. The iterative approach improved the phenotyping in each screening cycle. Without an iterative approach, the positive screening rate (PSR) was expected to dip below the 20% measured in the first cycle; however, the cyclical approach increased the PSR to 23%. Conclusions Our study demonstrates that dynamic phenotyping can facilitate recruitment for prospective clinical study. Future directions include improved informatics infrastructure and governance policies to enable real-time updates to research repositories, tooling for EHR annotation, and methodologies to reduce human annotation.

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Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform

Cited by 6 publications

References 19 publications

Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Phenotyping to Facilitate Accrual for a Cardiovascular Intervention

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