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Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

机译：患者级元数据的基于云的结肠镜检查的融合便于大规模机器学习

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摘要

Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos.

机译：背景和学习目标全长内窥镜手术的存储越来越受欢迎。为了便于大规模机器学习（ML）专注于临床结果，必须将这些视频与电子健康记录（EHR）中的患者级数据合并。我们的目的是提供一种准确地将患者级EHR数据与云存储的结肠镜检查进行准确地联系起来的方法。

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期刊名称 Endoscopy International Open
作者
Rajesh N. Keswani; Daniel Byrd; Florencia Garcia Vicente; J. Alex Heller; Matthew Klug; Nikhilesh R. Mazumder; Jordan Wood; Anthony D. Yang; Mozziyar Etemadi;
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年(卷),期 2021(9),2
年度 2021
页码 E233–E238
总页数 6
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7. Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning [O] . Rajesh N. Keswani, Daniel Byrd, Florencia Garcia Vicente, 2021

机译：患者级元数据的基于云的结肠镜检查的融合，便于大规模机器学习

Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

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