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英语角阅读:机器学习—人工智能

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乔治全球健康研究所的一项新研究发现,机器学习——人工智能的一个领域,使用统计技术使计算机系统“学习”数据——可以用于分析电子健康记录和预测急诊医院入院的风险。牛津马丁学校深医学项目资助了EALTH。
 
 
 
 
发表在《PLOS.》杂志上的这项研究表明,使用这些技术可以帮助卫生从业者准确地监测患者面临的风险,并制定措施避免意外入院,这是卫生保健支出的主要来源。
 
 
 
 
英国乔治研究所(George Institute UK)前数据科学家拉希米安(Fatemeh Rahimian)说,2017年,英国有590多万急诊入院记录,其中很大一部分是可以避免的。
 
 
 
 
我们希望提供一种工具,使医护人员能够准确监测患者面临的风险,从而围绕患者筛选和积极护理做出更好的决策,从而有助于减轻急诊入院的负担。
 
 
 
 
这项研究从1985年到2015年对460万患者进行,使用的是来自英国临床实践研究数据链的链接电子健康记录。考虑了各种各样的因素,包括年龄、性别、种族、社会经济地位、家族史、生活方式因素、合并症、药物和婚姻状况,以及首次诊断、最后一次使用卫生系统和最新实验室测试以来的时间。
 
 
 
 
使用更多的变量结合关于其时机的信息,机器学习模型被发现提供了比之前使用的任何模型更稳健的急诊医院入院风险预测。
 
 
 
 
拉希米安说,我们的发现表明,对于包含丰富个人信息的大型数据集,机器学习模型的性能优于最好的传统统计模型之一。我们认为,这是因为机器学习模型自动捕捉并“学习”我们以前不知道的数据之间的交互。
 
 
 
 
机器学习模型是否能够导致其他医学领域风险预测的类似强改进,还需要进一步研究。


Machine learning – a field of artificial intelligence that uses statistical techniques to enable computer systems to ‘learn’ from data – can be used to analyse electronic health records and predict the risk of emergency hospital admissions, a new study from The George Institute for Global Health funded by the Oxford Martin School’s Deep Medicine programme has found.


The research, published in the journal PLOS Medicine, suggests that using these techniques could help health practitioners accurately monitor the risks faced by patients and put in place measures to avoid unplanned admissions, which are a major source of healthcare spending.


‘There were over 5.9 million recorded emergency hospital admissions in the UK in 2017, and a large proportion of them were avoidable,’ said Fatemeh Rahimian, former data scientist at The George Institute UK, who led the research.


‘We wanted to provide a tool that would enable healthcare workers to accurately monitor the risks faced by their patients, and as a result make better decisions around patient screening and proactive care that could help reduce the burden of emergency admissions.’


The study, of 4.6 million patients from 1985 to 2015, was conducted using linked electronic health records from the UK’s Clinical Practice Research Datalink. A wide range of factors was taken into account, including age, sex, ethnicity, socioeconomic status, family history, lifestyle factors, comorbidities, medication and marital status, as well as the time since first diagnosis, last use of the health system and latest laboratory tests.


Using more variables combined with information about their timing, machine learning models were found to provide a more robust prediction of the risk of emergency hospital admission than any models used previously.


‘Our findings show that with large datasets which contain rich information about individuals, machine learning models outperform one of the best conventional statistical models,’ Rahimian said. ‘We think this is because machine learning models automatically capture and ‘learn’ from interactions between the data that we were not previously aware of.’


Whether machine learning models can lead to similarly strong improvements in risk prediction in other areas of medicine requires further research.
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