使用机器学习预测自发性脑内出血患者的脑
Jiangbao Xu1, Cuijie Yuan1, Guofeng Yu1
1The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
Frontiers in neurology
|October 18, 2024
概括
机器学习模型可以在72小时内预测自发脑内出血 (SICH) 患者的脑. 渐变增强决策树 (GDBT) 显示出最佳表现,确定了早期干预的关键因素.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 在自发性脑内出血 (SICH) 中早期预测脑 edem 对于及时干预和改善患者结果至关重要.
- 使用可访问的临床数据开发准确的预测模型可以帮助管理SICH患者.
研究的目的:
- 开发和验证机器学习模型,用于预测SICH患者在72小时内脑的变化.
- 确定影响脑的发展的关键临床参数.
主要方法:
- 一项观察性研究包括215名患者,随机分为培训 (N=150) 和验证 (N=65) 队列.
- 支持矢量机递归特征消除 (SVM-RFE) 和LASSO算法用于特征选择.
- 使用随机森林 (RF),梯度增强决策树 (GDBT),线性回归 (LR) 和XGBoost模型评估预测性能,并通过AUROC,AUPRC,准确性,F1分数,精度,回忆,灵敏度和特异性进行评估.
主要成果:
- 渐变增强决策树 (GDBT) 模型在验证队列中表现优越,AUC为0.654,超过LR (0.578) 和RF (0.624).
- 在验证组中,XGBoost表现相似 (AUC为0.660),但在训练组中,GDBT表现优于XGBoost (AUC为0.603比0.575).
- 沙普利添加式扩张 (SHAP) 分析确定了血清,高血量,下关节出血量,性别和左基底腺出血量作为GDBT模型中的关键预测因素.
结论:
- GDBT模型在预测SICH患者的72小时脑变化方面是有效的.
- 该模型可以帮助临床医生识别高风险个体,并指导临床决策,以改善患者管理.
相关概念视频
Hemorrhagic Stroke ll: Pathophysiology
A hemorrhagic stroke develops when a cerebral blood vessel ruptures, allowing blood to escape into the surrounding brain tissue, as in intracerebral hemorrhage (ICH), or into the subarachnoid space, as in subarachnoid hemorrhage (SAH). Because the skull is a rigid compartment, the sudden presence of extravascular blood rapidly increases intracranial pressure and compresses adjacent neural structures, leading to immediate tissue injury and impaired cerebral perfusion.Mass Effect and Primary...
Increased Intracranial Pressure ll: Pathophysiology
Increased intracranial pressure (ICP) refers to a potentially life-threatening rise in pressure inside the skull. This usually happens when there is a major change in the volume of brain tissue, blood, or cerebrospinal fluid (CSF) — the three components inside the skull. According to the Monro-Kellie doctrine, if the volume of one component increases, the volumes of the other components must decrease to maintain normal pressure. If this does not happen, ICP rises.The process often begins with...
Cerebral Edema l: Introduction
Cerebral edema is a pathological increase in brain water content that disrupts intracranial pressure regulation and impairs neurological function. Because the cranial vault is rigid, even modest increases in tissue volume can compromise cerebral perfusion, distort neural structures, and initiate secondary injury. Cerebral edema develops through four principal mechanisms: vasogenic, cytotoxic, interstitial, and ionic.Vasogenic EdemaVasogenic edema arises from disruption of the blood–brain...
Cerebral Edema ll: Pathophysiology
Vasogenic edema is a major form of cerebral edema characterized by abnormal accumulation of fluid in the brain’s extracellular space due to disruption of the blood–brain barrier (BBB). The BBB is a specialized structure composed of endothelial cells connected by tight junctions, supported by astrocytic endfeet and a basement membrane. Under normal conditions, it tightly regulates the movement of ions, proteins, and solutes between the bloodstream and brain parenchyma. When this barrier loses...
Cytotoxic Edema: Pathophysiology
Cytotoxic edema is a form of cerebral edema characterized by intracellular swelling of neurons, astrocytes, and other glial cells. It develops when the mechanisms responsible for maintaining ionic gradients across the cell membrane become impaired. Under normal physiological conditions, the sodium–potassium ATPase actively transports sodium ions out of the cell and potassium ions into the cell, preserving osmotic balance and enabling electrical signaling. This pump requires a continuous supply...


