Journal of Biomedical Informatics

Journal Information
ISSN / EISSN : 1532-0464 / 1532-0480
Current Publisher: Elsevier BV (10.1016)
Former Publisher:
Total articles ≅ 2,968
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Latest articles in this journal

Yan Huang, Xiaojin Li,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103744

Abstract:
Fast temporal query on large EHR-derived data sources presents an emerging big data challenge, as this query modality is intractable using conventional strategies that have not focused on addressing Covid-19-related research needs at scale. We introduce a novel approach called Event-level Inverted Index (ELII) to optimize time trade-offs between one-time batch preprocessing and subsequent open-ended, user-specified temporal queries. An experimental temporal query engine has been implemented in a NoSQL database using our new ELII strategy. Near-real-time performance was achieved on a large Covid-19 EHR dataset, with 1.3 million unique patients and 3.76 billion records. We evaluated the performance of ELII on several types of queries: classical (non-temporal), absolute temporal, and relative temporal. Our experimental results indicate that ELII accomplished these queries in seconds, achieving average speed accelerations of 26.8 times on relative temporal query, 88.6 times on absolute temporal query, and 1037.6 times on classical query compared to a baseline approach without using ELII. Our study suggests that ELII is a promising approach supporting fast temporal query, an important mode of cohort development for Covid-19 studies.
Xinyue Wang, Xiaoqian Jiang,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103714

The publisher has not yet granted permission to display this abstract.
, Dörthe Arndt, Jos De Roo, Erik Mannens
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103750

The publisher has not yet granted permission to display this abstract.
Juan Zhao, Monika E. Grabowska, Vern Eric Kerchberger, Joshua C. Smith, H. Nur Eken, , , S. Trent Rosenbloom, Kevin B. Johnson,
Journal of Biomedical Informatics, Volume 117, pp 103748-103748; doi:10.1016/j.jbi.2021.103748

The publisher has not yet granted permission to display this abstract.
Tara T. Helmer, Adam A. Lewis, Mark McEver, Francesco Delacqua, Cindy L. Pastern, Nan Kennedy, Terri L. Edwards, Beverly O. Woodward,
Journal of Biomedical Informatics, Volume 117, pp 103765-103765; doi:10.1016/j.jbi.2021.103765

Abstract:
The COVID-19 pandemic has resulted in an unprecedented strain on every aspect of the healthcare system, and clinical research is no exception. Researchers are working against the clock to ramp up research studies addressing every angle of COVID-19 – gaining a better understanding of person-to-person transmission, improving methods for diagnosis, and developing therapies to treat infection and vaccines to prevent it. The impact of the virus on research efforts is not limited to investigators and their teams. Potential participants also face unparalleled opportunities and requests to participate in research, which can result in a significant amount of participant fatigue. The Vanderbilt Institute for Clinical and Translational Research recognized early in the pandemic that a solution to assist researchers in the rapid identification of potential participants was critical, and thus developed the COVID-19 Recruitment Data Mart. This solution does not rest solely on technology; the addition of experienced project managers to support researchers and facilitate collaboration was essential. Since the platform and study support tools were launched on July 20, 2020, four studies have been onboarded and a total of 1693 potential participant matches have been shared. Each of these patients had agreed in advance to direct contact for COVID-19 research and had been matched to study-specific inclusion/exclusion criteria. Our innovative Data Mart system is scalable and looks promising as a generalizable solution for simultaneously recommending individuals from a pool of patients against a pool of time-sensitive trial opportunities.
Jayanta Kumar Das, Subhadip Chakraborty,
Journal of Biomedical Informatics; doi:10.1016/j.jbi.2021.103801

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Cong Sun, , , Yin Zhang, Hongfei Lin, Jian Wang
Journal of Biomedical Informatics; doi:10.1016/j.jbi.2021.103799

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, J.A. Maldonado, D. Boscá, S. Salas-García, M. Robles
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103747

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, Jonathan R. Brestoff, Ronald Jackups
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103756

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