[MEDSCAPE医学新闻]:机器能够提前5小时预测住院患者发生全身性感染 | 中国病理生理学会危重病医学专业委员会
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[MEDSCAPE医学新闻]:机器能够提前5小时预测住院患者发生全身性感染
2018年04月14日 时讯速递, 进展交流 暂无评论

Machine Predicts Inpatient Sepsis 5 Hours Sooner

Marcia Frellick

April 10, 2018

ORLANDO — Deep machine learning can help predict sepsis in hospitalized patients an average of 5 hours before they meet the clinical definition, new data show.

新的数据显示,深度机器学习能够在住院患者满足临床诊断标准前平均5小时预测全身性感染。

"Every hour delay in antibiotic therapy is associated with a 7% to 8% increase in mortality for those with septic shock," said investigator Cara O'Brien, MD, from Duke University in Durham, North Carolina.

研究者北卡罗来纳州Durham的Duke大学Cara O'Brien医生说到:“对于感染性休克患者,抗生素治疗每延误一个小时,病死率增加7%-8%。”

"Current tools to identify sepsis don't work very well," she told Medscape Medical News. They only look at the last set of variables, such as the last respiratory rate or last lab result.

“鉴别全身性感染的现有手段效果不佳”,她对Medscape医学新闻频道谈到。它们仅仅关注最新的参数指标,如最新呼吸频率或最新的实验室检查结果。

Deep learning looks at the trajectory of a patient's data throughout his or her hospital stay.

深度学习则可以纵览患者住院期间的所有数据。

"It's not looking at a single time point," O'Brien explained. "It's incorporating where the patient was 2 days ago, 1 day ago, 12 hours ago into the risk prediction."

“机器并非仅仅关注某一个时间点,”O'Brien解释道。“它能够整合2天前、1天前和12小时前的数据进行风险预测。”

Other tools used to predict sepsis are often compromised by alarm fatigue, said lead investigator Anthony Lin, a third-year medical student at the Duke Institute for Health Innovation.

用于预测全身性感染的其他工具常常受到误报警的困扰,首席研究者Anthony Lin说到。Lin是Duke健康创新学院的一名3年级医学生。

In fact, Lin and his colleagues found that alarms for sepsis in the Duke system were being canceled 63% of the time.

事实上,Lin和他的同事发现,Duke系统中有关全身性感染的报警中63%被取消。

Sepsis is difficult to detect because the same symptoms can indicate many diseases. Treatment is not difficult; the challenge is finding which patients are septic, Lin explained here at the Society of Hospital Medicine 2018 Annual Meeting.

全身性感染的鉴别非常困难,这是因为相同的症状可能提示很多疾病。治疗并不困难;难点在于发现哪位患者是全身性感染,Lin在医院医学学会2018年年会中说到。

In their retrospective study, the investigators used a deep-learning tool — which has been used in previous forms of speech-recognition programs, such as Google Translate — to look at fairly common predictor variables, such as vital signs, lab results, medication administrations, and orders.

在回顾性研究中,研究者采用了一种深度学习工具 — 这种工具已经用于语言识别计划,如Google Translate — 以寻找较为常见的预测指标,如生命体征、实验室检查结果、用药情况和医嘱。

They looked at all 43,046 adult inpatient admissions at Duke University Hospital from October 1, 2014 to December 31, 2015. An analysis of the millions of data points gleaned from the electronic health records of these patients yielded 83 predictor variables.

研究者回顾了2014年10月1日至2015年12月31日期间Duke大学医院收治的43046名成年住院患者。对于来自上述患者电子病历系统数以百万计的数据点的分析找到了83项预测指标。

The deep-learning tool outperformed the other tools currently available for the early detection of sepsis, Lin reported.

Lin报告,在早期鉴别全身性感染方面,深度学习工具的准确性超过了现有的其他工具。

And because the tool uses predictor variables commonly found in electronic health records, it could be used to identify patients experiencing cardiac or respiratory arrest in the hospital or a postoperative infection, the investigators note.

研究者指出,由于深度学习工具采用电子病历系统中常见的参数作为预测指标,因此可用于鉴别医院内心跳呼吸骤停或术后感染患者。

"We're currently investigating how this would play out in cardiogenic shock," Lin said.

“我们正在研究这一系统对于心源性休克的价值,”Lin说到。

The team is planning to launch the deep-learning intervention at Duke later this year. "It's one thing to build a model, but another to implement it in a health system and identify who would answer alarms and who would work up the patient," he noted.

研究团队计划于今年稍晚时候在Duke推出深度学习干预计划。“建立一个模型是一回事,而在医疗系统中实施又是另一回事,”他谈到。

Evolution of Informatics in Medicine 医学信息学的进步

"One of the things that excites me about this research is that it represents an evolution in the way we're using informatics in medicine," said Ethan Cumbler, MD, from the University of Colorado School of Medicine in Denver, who is director of the research, innovation, and vignettes section for the meeting.

Ethan Cumbler来自丹佛的科罗拉多大学医学院,她是大会研究及创新部门主任。她说:“这项研究令我兴奋的原因之一在于,它代表了我们在医学中应用信息学的一项进步。”

"If we can start using this form of artificial intelligence to not just store information within the electronic health record, but to derive from electronic health records new ways of understanding the data, we've created an entirely new way for medical informatics to support clinicians," Cumbler told Medscape Medical News. "That is exciting."

“如果我们开始将这种人工智能不仅仅用于信息存储,而是从电子病历系统中找到理解数据的新方法,我们就会为医学信息学帮助临床医生创造出一种全新的途径”,Cumbler对Medscape医学频道谈到。“那才真正令人兴奋。”

O'Brien, Lin, and Cumbler have disclosed no relevant financial relationships.

Society of Hospital Medicine (HM) 2018 Annual Meeting: Abstract 413603. Presented April 10, 2018.

Follow Medscape on Twitter @Medscape and Marcia Frellick @mfrellick

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