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机器学习如何改变生物医学。

How Machine Learning Will Transform Biomedicine.

机构信息

Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.

Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.

出版信息

Cell. 2020 Apr 2;181(1):92-101. doi: 10.1016/j.cell.2020.03.022.


DOI:10.1016/j.cell.2020.03.022
PMID:32243801
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7141410/
Abstract

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.

摘要

这篇观点文章探讨了机器学习在改进诊断和治疗方面的应用。我们概述了机器学习如何改变三个广泛的生物医学领域:临床诊断、精准治疗和健康监测,其目标是通过一系列疾病和正常衰老过程来保持健康。对于每个领域,我们都讨论了机器学习应用成功的早期实例,以及机器学习的机会和挑战。当这些挑战得到解决时,机器学习有望实现严格的、基于结果的医学,具有不断适应个体和环境差异的检测、诊断和治疗策略。

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本文引用的文献

[1]
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

Lancet Digit Health. 2019-10

[2]
A Deep Learning Approach to Antibiotic Discovery.

Cell. 2020-2-20

[3]
The exposome and health: Where chemistry meets biology.

Science. 2020-1-24

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Prediction of gestational diabetes based on nationwide electronic health records.

Nat Med. 2020-1-13

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Nature. 2020-1-1

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IEEE Trans Med Imaging. 2020-4

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Cell. 2019-8-29

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