机械呼吸机脱机结果的预测——一种深度学习方法

Kuei-Hung Shen, Yun-Ju Yu, Szu-Yin Chen, En-Ming Chang, Hsiu-li Wu, Cheng-Kuan Lin, Yu-Chee Tseng
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引用次数: 0

摘要

机械通气(MV)是一种帮助患者呼吸或在患者无法自主呼吸时为患者呼吸的治疗方法。医生和治疗师根据生命体征和各种医学检查结果确定患者是否准备好脱离机械通气。本研究探讨了深度学习方法应用于多种生理参数预测机械呼吸机脱机结果。我们的实验表明,与同类研究相比,在有限的样本和较少的特征下,验证精度为0.682。我们希望通过收集更多的数据和特性来提高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Mechanical Ventilator Weaning Outcome – A Deep Learning Approach
Mechanical Ventilation (MV) is a type of therapy that helps patients breathe or breathes for patients when they can’t breathe on their own. Doctors and therapists determine whether a patient is ready for weaning of mechanical ventilation based on vitals and various medical test results. This study explores deep learning methods applied to prediction of mechanical ventilator weaning outcome using multiple physiological parameters. Our experiments showed a validation accuracy of 0.682 with limited samples and less features compared to similar studies. We expect to see improved performance with more data and features collected.
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