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基于人工智能的模型可以预测创伤性脑损伤患者的脱补偿

Ricardo Ribeiro¹, Inês Neves², Hélder P Oliveira¹

¹INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FCUP - Faculty of Sciences of the University of Porto, Porto, Portugal.

Computers in biology and medicine

|January 9, 2025

概括

人工智能 (AI) 模型可以使用电子健康记录 (EHR) 预测创伤性脑损伤 (TBI) 病例中的患者减补偿. 这种方法有助于临床决策,并改善了ICU中TBI患者的护理.

科学领域:

医疗信息学医疗信息学
神经学神经学
人工智能的人工智能

背景情况:

创伤性脑损伤 (TBI) 带来了显著的死亡风险,在严重病例中达到30-40%.
对TBI并发症的有效临床决策仍然是一个挑战.

研究的目的:

开发和评估用于预测TBI患者脱补偿的AI模型.
通过使用数据驱动方法来增强患者护理和临床决策.

主要方法:

利用来自MIMIC-III数据集的2261名TBI患者电子健康记录 (EHR) 的序列数据.
使用后勤回归 (LR),长短期内存 (LSTM) 和变压器架构进行预测.
探索了特征集,缺失数据归算和数据失衡技术.

主要成果:

LSTM模型实现了高性能,AUROC分数为0.918 (原始功能) 和0.929 (附加功能与类权重).
这项研究证明了使用EHR时间序列数据用于预测建模的可行性.

结论:

使用EHR时间序列数据的LSTM模型对预测TBI患者减补偿有效.
这种预测能力可以作为必要临床干预的早期指标.

关键词:

减偿预测减偿预测深度学习是一种深度学习.电子健康记录数据数据电子健康记录数据创伤性脑损伤是一种创伤性脑损伤.

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Traumatic Brain Injury l: Introduction

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DefinitionTraumatic brain injury, or TBI, is a disturbance of normal brain function induced by an external mechanical force, such as a direct blow to the head or a penetrating injury. It can affect both brain structure and function, producing a wide range of clinical outcomes. TBI is a heterogeneous condition, meaning its effects may differ based on the type, location, and severity of the injury.Basis of ClassificationTBI is classified based on severity, injury mechanism, or pathophysiology. In...