Background

Despite the expanding use of machine learning (ML) in fields such as finance and marketing, its application in the daily practice of clinical medicine is almost non-existent. In this systematic review, we describe the various areas within clinical medicine that have applied the use of ML to improve patient care.

Methods

A systematic review was performed in accordance with the PRISMA guidelines using Medline(R), EBM Reviews, Embase, Psych Info, and Cochrane Databases, focusing on human studies that used ML to directly address a clinical problem. Included studies were published from January 1, 2000 to May 1, 2018 and provided metrics on the performance of the utilized ML tool.

Results

A total of 1909 unique publications were reviewed, with 378 retrospective articles and 8 prospective articles meeting inclusion criteria. Retrospective publications were found to be increasing in frequency, with 61 % of articles published within the last 4 years. Prospective articles comprised only 2 % of the articles meeting our inclusion criteria. These studies utilized a prospective cohort design with an average sample size of 531.

Conclusion

The majority of literature describing the use of ML in clinical medicine is retrospective in nature and often outlines proof-of-concept approaches to impact patient care. We postulate that identifying and overcoming key translational barriers, including real-time access to clinical data, data security, physician approval of “black box” generated results, and performance evaluation will allow for a fundamental shift in medical practice, where specialized tools will aid the healthcare team in providing better patient care.

中文翻译：

---

机器学习对患者护理的影响：系统评价。

背景

尽管机器学习（ML）在金融和市场营销等领域的使用越来越广泛，但在临床医学的日常实践中几乎没有这种应用。在这篇系统的综述中，我们描述了应用ML改善患者护理的临床医学领域。

方法

根据PRISMA指南，使用Medline（R），EBM评论，Embase，Psych Info和Cochrane数据库进行了系统的综述，重点在于使用ML直接解决临床问题的人体研究。纳入的研究发表于2000年1月1日至2018年5月1日，并提供了有关所用ML工具性能的指标。

结果

共审查了1909种独特的出版物，其中378篇回顾性文章和8篇符合纳入标准的前瞻性文章。发现回顾性出版物的频率在增加，最近四年内发表的文章占61％。符合我们纳入标准的预期文章仅占2％。这些研究采用前瞻性队列设计，平均样本量为531。

结论

大部分描述ML在临床医学中使用的文献本质上都是回顾性的，并且通常概述了影响患者护理的概念验证方法。我们假设发现并克服关键的翻译障碍，包括实时访问临床数据，数据安全性，医师对“黑匣子”生成结果的认可以及性能评估，将使医学实践发生根本性转变，在此方面，专用工具将为您提供帮助医疗团队提供更好的患者护理。