LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization.

IF 2.3 4区 医学 Q3 BIOPHYSICS
Longfei Liu, Yujie Hang, Rongqin Chen, Xianliang He, Xingliang Jin, Dan Wu, Ye Li
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引用次数: 0

Abstract

Objective. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a crucial role in saving patients' lives. Recent studies have focused on utilizing more complex models to improve predictive performance. However, these models are not suitable for clinical application due to limited computing resources for bedside monitors.Approach. To address this challenge, we propose an efficient lightweight dilated shuffle group network. It effectively incorporates shuffling operations into grouped convolutions on the channel and dilated convolutions on the temporal dimension, enhancing global and local feature extraction while reducing computational load.Main results. Our benchmarking experiments on the MIMIC-III and VitalDB datasets, comprising 6036 samples from 1304 patients and 2958 samples from 1047 patients, respectively, demonstrate that our model outperforms other state-of-the-art lightweight CNNs in terms of balancing parameters and computational complexity. Additionally, we discovered that the utilization of multiple physiological signals significantly improves the performance of AHE prediction. External validation on the MIMIC-IV dataset confirmed our findings, with prediction accuracy for AHE 5 min prior reaching 93.04% and 92.04% on the MIMIC-III and VitalDB datasets, respectively, and 89.47% in external verification.Significance. Our study demonstrates the potential of lightweight CNN architectures in clinical applications, providing a promising solution for real-time AHE prediction under resource constraints in ICU settings, thereby marking a significant step forward in improving patient care.

LDSG-Net:用于预测重症监护室住院期间急性低血压发作的高效轻量级卷积神经网络。
急性低血压发作(AHE)是重症监护病房(ICU)最严重的并发症之一。及时准确的 AHE 预测系统能为临床医生提供充足的时间采取适当的治疗措施,在挽救患者生命方面发挥着至关重要的作用。最近的研究侧重于利用更复杂的模型来提高预测性能。然而,由于床旁监护仪的计算资源有限,这些模型并不适合临床应用。为了应对这一挑战,我们提出了一种高效的轻量级扩张洗牌组网络(LDSG-Net)。它有效地将洗牌操作纳入信道分组卷积和时间维度的扩张卷积中,在减少计算负荷的同时加强了全局和局部特征提取。我们在 MIMIC-III 和 VitalDB 数据集(分别包括来自 1304 名患者的 6036 个样本和来自 1047 名患者的 2958 个样本)上进行的基准实验表明,我们的模型在平衡参数和计算复杂度方面优于其他最先进的轻量级 CNN。此外,我们还发现,利用多种生理信号可显著提高 AHE 预测的性能。在 MIMIC-IV 数据集上进行的外部验证证实了我们的发现,在 MIMIC-III 和 VitalDB 数据集上,5 分钟前 AHE 的预测准确率分别达到 93.04% 和 92.04%,外部验证准确率为 89.47%。我们的研究证明了轻量级 CNN 架构在临床应用中的潜力,为 ICU 环境下资源限制条件下的实时 AHE 预测提供了一个很有前景的解决方案,从而在改善患者护理方面迈出了重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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