预测急性低血压发作:第 10 届年度 PhysioNet/Computers in Cardiology 挑战赛。

Computers in cardiology Pub Date : 2009-01-01
Gb Moody, Lh Lehman
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引用次数: 0

摘要

今年的 PhysioNet/Computers in Cardiology Challenge(心脏病学计算机挑战赛)旨在激励开发用于识别面临急性低血压发作(AHE)紧迫风险的重症监护病房(ICU)病人的方法,其动机是提高这些病人的护理和存活率。参与者被要求根据 MIMIC II 数据库中两组重症监护病房患者的记录,提前一小时预测 AHE 的发生,预测数据包括一小时预测窗口前至少 10 小时的生理波形、时间序列和相关临床数据。在事件 1 中,大多数参与者都能准确无误地识别出在接受加压药物治疗的 10 位高风险患者中,有哪五位患者在预报窗口期间出现了 AHE。在事件 2 中,参与者能够对 40 名不同患者中的多达 37 人(93%)进行正确分类,其中包括几乎所有出现 AHE 的患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Acute Hypotensive Episodes: The 10th Annual PhysioNet/Computers in Cardiology Challenge.

This year's PhysioNet/Computers in Cardiology Challenge aimed to stimulate development of methods for identifying intensive care unit (ICU) patients at imminent risk of acute hypotensive episodes (AHEs), motivated by the possibility of improving care and survival of these patients. Participants were asked to forecast the occurrence of an AHE up to an hour in advance, in two groups of ICU patient records from the MIMIC II Database, drawing on data that included at least 10 hours of physiologic waveforms, time series, and accompanying clinical data prior to the one-hour forecast window. In event 1, most participants were able to identify without errors, in a group of 10 high-risk patients receiving pressor medication, which five of the patients experienced AHEs during the forecast window. In event 2, participants were able to classify correctly as many as 37 (93%) of a diverse group of 40 patients, including nearly all of those who experienced AHEs.

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