FT-GAT:图形神经网络用于预测机械呼吸试验患者自发呼吸试验的成功
Geun-Hyeong Kim1, Jae-Woo Kim1, Ka Hyun Kim1
1Medical AI Research Team, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
Computer methods and programs in biomedicine
|June 19, 2023
概括
这项研究开发了一种人工智能模型,FT-GAT,用于预测机械呼吸 (MV) 患者的成功自发呼吸试验 (SBTs). FT-GAT有助于医生在早期释放,潜在地改善患者的结果和减少通风持续时间.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 肺和呼吸系统医学 肺和呼吸系统医学
背景情况:
- 在重症监护室 (ICU) 住院后,将患者从机械通风器 (MV) 中释放出来是具有挑战性的.
- 现有的断奶协议和医生的临床决定在预测成功的自发呼吸试验 (SBT) 中存在局限性.
研究的目的:
- 开发一种人工智能 (AI) 模型来预测自发呼吸试验 (SBT) 的成功.
- 帮助ICU医生做出明智的决策,为患者早日从MV中获释.
主要方法:
- 来自652名ICU患者的回顾性数据收集 (2020年7月至2022年7月).
- 使用了一种新的深度学习模型,特征标记器图形注意力网络 (FT-GAT),用于表式数据分析.
- 基于特征相关性的工程图形结构,用于增强图形分析.
主要成果:
- FT-GAT的AUROC为0.80,超过了传统模型 (0.69-0.79) 和临床指标 (0.65-0.66).
- 该模型有效地预测了SBT的成功,使用了11个数值和2个分类患者特征.
结论:
- FT-GAT模型显示了及时识别可能在SBTs中成功的患者的潜力.
- 早期检测可以帮助预防长期使用MV,并改善重症监护机构的整体患者结果.
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