The Veterans Affairs Precision Oncology Data Repository, a Clinical, Genomic, and Imaging Research Database

Elbers, Danne C.; Fillmore, Nathanael; Fc, Sung; Ganas, Spyridon; Prokhorenkov, Andrew; Meyer, Christopher; Hall, Robert B.; Ajjarapu, Samuel; Chen, Daniel C.; Meng, Frank; Grossman, Robert L.; Brophy, Mary; Nhan, V.

doi:10.1016/j.patter.2020.100083

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…This data consists of long-term clinical data from the nationwide Veterans Affairs eHealth record, medical photography data, tumor sequencing targets, pathology slides, and the Veterans Affairs central cancer registry as well as CT (computer tomography) scans. A subset of this data storage is available in GDC (genomic data commune) and the TCIA (The Cancer Imaging Archive) [ 67 ].…”

Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

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Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

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Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

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“…The Pandemic Response Consortium is a private-public partnership that is operated by the Open Commons Consortium (OCC), part of an independent 501(c)(3) not-for-profit corporation that operates several other data commons, including the BloodPAC Data Commons (5) and the Veterans Precision Oncology Data Commons (6).…”

Section: Methodsmentioning

confidence: 99%

The Pandemic Response Commons

Trunnell

Frankenberger

Hota

et al. 2022

Preprint

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ObjectiveA data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic.MethodsThe pandemic response commons was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the pandemic response commons.ResultsWe developed a general pandemic response commons and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures and controls based upon NIST SP 800-53. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data.Discussion and conclusionGiven the various legal and data agreements required to operate a data commons, a pandemic response commons is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a Pandemic Response Commons is designed to be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing of regional data.

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“…Analysis in the US-VA system was conducted with approval from the VA Boston Healthcare System Institutional Review Board. For the US-VA system, we used data from the VA Corporate Data Warehouse (CDW), which collates electronic health record and cancer registry information on veterans treated at VA facilities nationwide (Price, Shea, and Gephart 2015), using methods similar to prior work (Elbers et al 2020; Chang et al 2022)(Wu et al 2022). The CDW includes electronic health record data from 1999 to the present originating from the comprehensive range of primary care and specialized services the VA provides at its inpatient and outpatient facilities, as well as claims data for care received at outside facilities and paid for by the VA. Each outpatient visit in the database is associated with the date it occurred, and each inpatient visit is associated with an admission and discharge date.…”

Section: Methodsmentioning

confidence: 99%

Pancreatic cancer risk predicted from disease trajectories using deep learning

Yuan

et al. 2021

Preprint

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Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be facilitated by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a large corpus of more than 6 million patient records spanning 40 years with 24,000 pancreatic cancer cases in the Danish National Patient Registry. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories. In addition, the models predict the risk of cancer occurrence in time intervals of 3 to 60 months duration after risk assessment. For cancer occurrence within 12 months, the performance of the best model trained on full trajectories (AUROC=0.91) substantially exceeds that of a model without time information (AUROC=0.81). For the best model, lower performance (AUROC=0.86) results when disease events within a 3 month window before cancer diagnosis are excluded from training, reflecting the decreasing information value of earlier disease events. These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of real-world population-wide clinical screening trials, in which high risk patients are assigned to serial imaging and measurement of blood-based markers to facilitate earlier cancer detection. AI on real-world clinical records has the potential to shift focus from treatment of late- to early-stage cancer, benefiting patients by improving lifespan and quality of life.

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The Veterans Affairs Precision Oncology Data Repository, a Clinical, Genomic, and Imaging Research Database

Cited by 14 publications

References 16 publications

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Influential Usage of Big Data and Artificial Intelligence in Healthcare

The Pandemic Response Commons

Pancreatic cancer risk predicted from disease trajectories using deep learning

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