Evidence map›Paper›PMID 35040609›Full record

ArticleYonsei medical journal2022

Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud.

Kyu Hee Lee, Erdenebayar Urtnasan, Sangwon Hwang, Hee Young Lee, Jung Hun Lee, Sang Baek Koh, Hyun Youk

Abstract read
In one paragraph

Article in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Medical Big Data Storage in Precision Medicine: A Systematic Review.Journal of biomedical physics & engineering · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kyu Hee LeeArtificial Intelligence Big Data Medical Center, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0002-7378-3697
Erdenebayar UrtnasanArtificial Intelligence Big Data Medical Center, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0002-3493-9724
Sangwon HwangArtificial Intelligence Big Data Medical Center, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0001-8666-7479
Hee Young LeeBigdata Platform Business Group, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0003-3254-2261
Jung Hun LeeDepartment of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0002-4799-6277
Sang Baek KohBigdata Platform Business Group, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0001-5609-6521
Hyun YoukBigdata Platform Business Group, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0002-4631-1504

Funding

Korea Health Industry Development Institute HI19C1035Ministry of Science, ICT and Future Planning 2020-Data-W123National Research Foundation of Korea NRF-2020R1I1A1A01066463
6 · The paper itself

Abstract

purposeWe propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system. MATERIALS AND

methodsThe Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis.

resultsThe big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB.

conclusionThe Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.

Indexed as

EcosystemPrecision MedicineChronic DiseaseDelivery of Health CareHospitalsHumansbig datadigital healthLifelogprecision medicine

Identifiers

PMID35040609
PMCPMC8790588

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.