Comments on Contemporary Uses of Machine Learning for Electronic Health Records

Q2 Medicine

North Carolina Medical Journal Pub Date : 2024-07-11 DOI:10.18043/001c.120570

Jordan Bryan, Didong Li

引用次数: 1

Abstract

Various decisions concerning the management, display, and diagnostic use of electronic health records (EHR) data can be automated using machine learning (ML). We describe how ML is currently applied to EHR data and how it may be applied in the near future. Both benefits and shortcomings of ML are considered.

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关于机器学习在电子健康记录中的当代应用的评论

有关电子健康记录（EHR）数据的管理、显示和诊断使用的各种决策都可以通过机器学习（ML）实现自动化。我们介绍了目前如何将 ML 应用于电子病历数据，以及在不久的将来可能如何应用。我们考虑了 ML 的优点和缺点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

North Carolina Medical Journal Medicine-Medicine (all)

CiteScore

1.40

自引率

0.00%

发文量

121

期刊介绍： NCMJ, the North Carolina Medical Journal, is meant to be read by everyone with an interest in improving the health of North Carolinians. We seek to make the Journal a sounding board for new ideas, new approaches, and new policies that will deliver high quality health care, support healthy choices, and maintain a healthy environment in our state.