基于大数据统计分析的医疗血液扩张测量

Xin Wang, Qiang Jiang, Huixiao Chu, Xudong Pu and Tong Cheng
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引用次数: 0

摘要

本研究旨在探讨出血性脑卒中患者血肿扩大、水肿发展与患者预后之间的关系。利用 160 例出血性脑卒中患者的临床信息、影像学特征和预后数据,构建了多个预测模型,以研究这些患者的病理进展和预后。具体来说,数据预处理用于计算每次检查之间的时间间隔,并选择 48 小时内的数据来分析血肿体积及其百分比的变化。这有助于确定发病后 48 小时内血肿的扩大情况,并将结果记录在专用表格中。采用 XGBoost 模型对测试和训练数据集进行训练,以开发血肿扩张预测模型。经过评估,该模型预测所有患者(sub001-sub160)血肿扩大的准确率为 75%。这项研究强调了使用 XGBoost 等高级预测模型来加强出血性中风护理中的预后评估和临床决策的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measurement of medical haemodilatation based on statistical analysis of big data
This study aims to explore the association between hematoma expansion, edema development, and patient prognosis in hemorrhagic stroke patients. Utilizing clinical information, imaging characteristics, and prognostic data from 160 hemorrhagic stroke patients, several predictive models were constructed to examine the pathological progression and outcomes of these patients. Specifically, data preprocessing was employed to calculate the time intervals between each examination and select data within 48 hours for analyzing changes in hematoma volume and its percentage. This facilitated the determination of hematoma expansion within the first 48 hours post-onset, with results documented in a dedicated table. Employing the XGBoost model, both the test and training datasets were trained to develop a predictive model for hematoma expansion. Upon evaluation, the model demonstrated a 75% accuracy rate in predicting hematoma expansion across all patients (sub001-sub160). This study underscores the potential of using advanced predictive modeling, such as XGBoost, to enhance the prognosis assessment and clinical decision-making in hemorrhagic stroke care.
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